The Fully Bayesian Significance Test (FBST) is a coherent Bayesian significance test for sharp hypotheses. This paper explores the FBST as a model selection tool for general mixture models, and gives some computational experiments for Multinomial-Dirichlet-Normal-Wishart models.

Gene clustering is a useful exploratory technique to group together genes with similar expression levels under distinct cell cycle phases or distinct conditions. It helps the biologist to identify potentially meaningful relationships between genes. In this study, we propose a clustering method based on multivariate normal mixture models, where the number of clusters is predicted via sequential hypothesis tests: at each step, the method considers a mixture model of m components (m = 2 in the first step) (...) and tests if in fact it should be m - 1. If the hypothesis is rejected, m is increased and a new test is carried out. The method continues (increasing m) until the hypothesis is accepted. The theoretical core of the method is the full Bayesian significance test, an intuitive Bayesian approach, which needs no model complexity penalization nor positive probabilities for sharp hypotheses. Numerical experiments were based on a cDNA microarray dataset consisting of expression levels of 205 genes belonging to four functional categories, for 10 distinct strains of Saccharomyces cerevisiae. To analyze the method’s sensitivity to data dimension, we performed principal components analysis on the original dataset and predicted the number of classes using 2 to 10 principal components. Compared to Mclust (model-based clustering), our method shows more consistent results. (shrink)

The Fully Bayesian Significance Test (FBST) is a coherent Bayesian significance test for sharp hypotheses. This paper proposes the FBST as a model selection tool for general mixture models, and compares its performance with Mclust, a model-based clustering software. The FBST robust performance strongly encourages further developments and investigations.

Acoustic emission technique is a common approach to identify the damage of the refractories; however, there is a complex problem since there are as many as fifteen involved parameters, which calls for effective data processing and classification algorithms to reduce the level of complexity. In this paper, experiments involving three-point bending tests of refractories were conducted and AE signals were collected. A new data processing method of merging the similar parameters in the description of the damage and reducing the (...) dimension was developed. By means of the principle component analysis for dimension reduction, the fifteen related parameters can be reduced to two parameters. The parameters were the linear combinations of the fifteen original parameters and taken as the indexes for damage classification. Based on the proposed approach, the Gaussian mixture model was integrated with the Bayesian information criterion to group the AE signals into two damage categories, which accounted for 99% of all damage. Electronic microscope scanning of the refractories verified the two types of damage. (shrink)

This article describes the Full Bayesian Significance Test for the problem of separate hypotheses. Numerical experiments are performed for the Gompertz vs. Weibull life span test.

Abstract: The Pull Bayesian Significance Test (FBST) for precise hy- potheses is applied to a Multivariate Normal Structure (MNS) model. In the FBST we compute the evidence against the precise hypothesis. This evi- dence is the probability of the Highest Relative Surprise Set (HRSS) tangent to the sub-manifold (of the parameter space) that defines the null hypothesis. The MNS model we present appears when testing equivalence conditions for genetic expression measurements, using micro-array technology.

This paper presents an in-depth analysis of tennis match scene classification using an adaptive Gaussian mixture model parameter estimation simulation algorithm. We divided the main components of semantic analysis into type of motion, distance of motion, speed of motion, and landing area of the tennis ball. Firstly, for the problem that both people and tennis balls in the video frames of tennis matches from the surveillance viewpoint are very small, we propose an adaptive Gaussian mixture model parameter estimation (...) algorithm, which has good accuracy and speed on small targets. Secondly, in this paper, we design a sports player tracking algorithm based on role division and continuously lock the target player to be tracked and output the player region. At the same time, based on the displacement information of the key points of the player’s body and the system running time, the distance and speed of the player’s movement are obtained. Then, for the problem that tennis balls are small and difficult to capture in high-speed motion, this paper designs a prior knowledge-based algorithm for predicting tennis ball motion and landing area to derive the landing area of tennis balls. Finally, this paper implements a prototype system for semantic analysis of real-time video of tennis matches and tests and analyzes the performance indexes of the system, and the results show that the system has good performance in real-time, accuracy, and stability. (shrink)

To estimate causal relationships, time series econometricians must be aware of spurious correlation, a problem first mentioned by Yule (1926). To deal with this problem, one can work either with differenced series or multivariate models: VAR (VEC or VECM) models. These models usually include at least one cointegration relation. Although the Bayesian literature on VAR/VEC is quite advanced, Bauwens et al. (1999) highlighted that “the topic of selecting the cointegrating rank has not yet given very useful and convincing results”. (...) The present article applies the Full Bayesian Significance Test (FBST), especially designed to deal with sharp hypotheses, to cointegration rank selection tests in VECM time series models. It shows the FBST implementation using both simulated and available (in the literature) data sets. As illustration, standard non informative priors are used. (shrink)

Conditional independence tests have received special attention lately in machine learning and computational intelligence related literature as an important indicator of the relationship among the variables used by their models. In the field of probabilistic graphical models, which includes Bayesian network models, conditional independence tests are especially important for the task of learning the probabilistic graphical model structure from data. In this paper, we propose the full Bayesian significance test for tests of conditional independence (...) for discrete datasets. The full Bayesian significance test is a powerful Bayesian test for precise hypothesis, as an alternative to the frequentist’s significance tests (characterized by the calculation of the p-value). (shrink)

Failings of a categorical systemFor decades, standardized classification systems have attempted to define psychiatric disorders in our mental health care system, with the Diagnostic and Statistical Manual of Mental Disorders (5th ed.; DSM-5; American Psychiatric Association (APA), 2013) and International Statistical Classification of Diseases and Related Health Problems 10th revision (ICD-10; World Health Organization, 2010) being internationally best-known. One of the major advantages of the DSM must be that it has seriously diminished the international linguistic confusion regarding psychiatric disorders. Since (...) its introduction, it contributed extensively towards one common international language for defining and conceptualizing psychiatric disorders. Strikingly, within the field of psychological testing a similar step forward seems to have not yet been taken. At present, there exists no international standard for the use of psychological tests that takes the definition of a specific symptom as listed in DSM-5 as its starting point, and reliably and validly measures this symptom. Rather, the majority of tests are measuring constructs consisting of a multitude of symptoms. For example, the Beck’s Depression Inventory (Beck et al., 1996) measures core symptoms of depression summing up to a depression score. Accordingly, we believe it is time for a change. The diagnostics of psychiatric disorders, where disorders are defined as nosological units with a single cause, a single organic substrate, and a single time course, has been problematic for centuries. The field of psychiatry has always been ambivalent about its desire to follow a medical model (Blaney, 2015), but afflicted due to its definitions of pathology. The definition of a psychiatric disorder in DSM-5 offers little room for a clear cut pathogenesis and harsh demarcation of syndromes. This is reflected in the DSM-5, where it states: “A mental disorder is a syndrome characterized by clinically significant disturbance in an individual’s cognition, emotion regulation, or behavior that reflects a dysfunction in the psychological, biological, or developmental processes underlying mental functioning” (APA, 2013, p.20). An unfortunate and recognized consequence of this definition within the current system is that many symptoms overlap within categories of psychiatric disorders and patients end up diagnosed with many co-morbid disorders.Two fundamental problemsRegardless of new attempts to improve the diagnostics of psychiatric disorders, the DSM-5 represents the status quo. Consequently, the distinction between diagnosis and classification remains substantial and more than a discussion on semantics. We identify two fundamental problems within the current framework. Generally, a psychiatric diagnosis is considered to be descriptive (Hengeveld and Schudel, 2003). The clinician will describe a syndrome (its nature, timespan, and severity) within the framework of a disorder, and often include a differential diagnosis, predisposing protective or vulnerability factors, and provoking or maintaining factors. Within the DSM-5 framework, a similar ‘approach to clinical case formulation’ is taken (APA, 2013, p. 19). Evidently, a descriptive diagnosis has a hypothetical character. It consists of the clinician’s hypothesis and is based on his or hers professional considerations according to the abovementioned factors. Once the clinician has formulated a descriptive diagnosis, it is then complementary ‘translated’ into a DSM-5 classification. Herein lies the problem. For the layman, the existence of this distinction between diagnosis and classification is usually unknown. Preceding treatment, the clinician will give the client a classification of the existing psychiatric problems and the client may attribute more meaning to it than appropriate, thereby fostering the risk of reification. Second, the DSM-5 is a categorical system. Thus, individual disorders are regarded as discrete units – ‘you either have it, or you don’t’. DSM-5 states about this: “(…) scientific evidence places many, if not most, disorders on a spectrum with closely related disorders that have shared symptoms, shared genetic and environmental risk factors (…)”. And “(…) we have come to recognize that the boundaries between disorders are more porous than originally perceived.” (APA, 2013, p. 6). This leads to a fundamental problem. Because the overwhelming majority of psychiatric disorders examined thus far using taxometric methods appear to be dimensional in nature (Haslam, 2003; Widiger and Samuel, 2005), consequently all of their categorizations become artificial and debatable. Even though DSM-5 took a modest step towards a more dimensional approach, its core remains categorical. To illustrate, consider the Borderline Personality Disorder (BPD). This classification consists of nine diagnostic criteria of which a minimum of five need to be present for the diagnosis of BPD. A simple numerical combination algorithm leads to a staggering number of 256 distinct presentations of BPD (Albion et al., 2013). Due to this chameleon-like nature, the disorder’s diagnostic validity becomes questionable. Strikingly, this number is relatively small when compared to other conditions, e.g., there are 636,120 ways to have posttraumatic stress disorder (Galatzer-Levy and Bryant, 2013).DSM-5 does propose an alternative model for personality disorders based on personality functioning and traits (APA, 2013, p. 761), as a possible answer to the problem that most patients fit with multiple co-morbid personality disorders or to the category of personality disorder not otherwise specified. In November 2012, the chair committee of APA decided to move this alternative model to section III of DSM-5 and to sustain the categorical system in section II. A potential solutionDSM-5 and its predecessors have brought about an invaluable improvement regarding the formulation of a common international language for psychiatric disorders. However, the individual disorders fit poorly with its starting point of discrete units and strict boundaries. Van Os (2014) has argued for a better balance between the categorical and personalized aspects of psychiatric disorders. We argue for a two-step approach.Particularly due to the ‘weak boundaries’ between disorders, it might be beneficial to limit ourselves by merely categorizing clients into the main categories of the DSM-5 according to their most prominent symptoms (Van Os, 2014), i.e. their main complaint. In other words, clinicians could first ask themselves whether the symptomatology is concerning a neurodevelopmental disorder, or a bipolar and related disorder, or a depressive disorder, and so on. It will drastically reduce the over 400 classifications in DSM-5 down to 20 categories. Van Os (2014) even argues to combine some of the main categories, reducing this number even further to 15 categories. The advantages of reducing the over 400 classifications are fivefold: 1) a syndrome, being an aggregation of symptoms, creates a false relation between symptoms that are that are already heterogeneous themselves; 2) 15 broad categories are functional: it results into a very heterogeneous group of clients within a category and thus prevents stereotypes and invites further personalizing of complaints; and 3) a dimensional measure of a symptom rather than a syndrome will correspond better with the client regardless of it not providing the complete picture. Focusing on the main complaint will indicate where there is an immediate need for care (Van Os, 2014); 4) it will allow clinicians to recognise subthreshold conditions more easily (Magruder and Calderone, 2000); and 5) it might facilitate the development of clear demarcations between normal and abnormal functioning (Kessler, 2002; Widiger and Samuel, 2005). Please, see also Table 1.This also fits well within scientific advances, as thus far strongest evidence has been found for a distinction between internalizing and externalizing disorders, even superseding these 15 main categories (DSM-5; APA, 2013, p.13, but see Weiss et al., 1998). The second step would concern the personalized aspect, which we argue to define on a level of core symptoms of the specific main category, to be agreed upon later. To assess these core symptoms, the field of psychological testing could develop an internationally applicable instrument to reliably and validly measure each (core) symptom on a dimensional scale. Many of these instruments are readily available and have proven its psychometric properties within scientific research. Elements from these instruments could be easily transformed to work within the new system. Within the field of psychology, this approach could aid development of a common international language to define symptoms, analogous to the field of psychiatry. Importantly, current psychological testing mainly implies the use of self-report measures. This leads to an abundance of auto-amnestic information. To increase the reliability and validity of psychological tests to assess DSM-5 symptoms, it may be important to add two additional information resources. This would entail psychological tests where a next of kin answers questions about the symptom of the client, and psychological tests that include the clinicians’ judgment, such as structured clinician-based interviews. Conversely, we also need to acknowledge the well-known limitations of clinical judgment (e.g., Faust, 1986; Garb, 1998). Advantages of a new frameworkThe changes we suggest have big implications and are not easily implemented in the current framework. Clients may prefer the comfort of a single clear label, similar to what they are used to in other areas of medicine. Health insurance providers and policy makers may argue for single labels as well. Therefore, it would require a lot of explanation from our clinicians to promote a change. Our suggestions for broader categories may not disambiguate the current situation better than the existing approach, but it will promote personalisation of health care. Difficulties in diagnostic reliability will remain in disorders that fall on the boundaries of the taxons, e.g., schizoaffective disorder versus bipolar disorder with psychotic characteristics, which may benefit from further study. There are also some politically tinged questions to keep in mind: How will we finance our health care, and how will we ensure a gradual transition from the current financing system, including clients who already have their labels? What about visitations from insurers and health inspection? How can they control for a good standard of quality in health care when considering the increasing heterogeneity? Nevertheless, we argue that the benefits outweigh the disadvantages. We will see a shift from a categorical to a personalized approach, which will lead to less (self) stigmatizing, less estrangement, and it will challenge reification thinking. DSM-V states that “a reformulation of research goals should also keep DSM-5 central to the development of dimensional approaches to diagnosis that will likely supplement or supersede current categorical approaches to diagnosis in coming years” (APA, 2013, p. 13). Our proposition for psychological testing on symptom level could contribute to this development. Furthermore, this new approach will do more justice to the heterogeneity of symptoms within and outside of classification categories, see also Table 1. Moreover, in this new approach we join forces of expert knowledge from the fields of psychiatry and psychological science. Finally, it offers new and potentially better opportunities to map health care needs. This in turn will lead to a better interface for allocation of appropriate treatment. It will present a clearer picture of when preventative care is preferred over treatment and vice versa. Importantly, health care needs will be more closely attuned to the ‘own story’ of the client.We argue for the national and international psychological associations in Europe and the United States of America to support the idea of a collective approach to develop an internationally standardized psychological testing battery to reliably assess all the core symptoms of the main categories in DSM-5. (shrink)

The purpose of this study is to develop and evaluate two diagnostic classification models (DCMs) for scoring ordinal item data. We first applied the proposed models to an operational dataset and compared their performance to an epitome of current polytomous DCMs in which the ordered data structure is ignored. Findings suggest that the much more parsimonious models that we proposed performed similarly to the current polytomous DCMs and offered useful item-level information in addition to option-level information. We then performed a (...) small simulation study using the applied study condition and demonstrated that the proposed models can provide unbiased parameter estimates and correctly classify individuals. In practice, the proposed models can accommodate much smaller sample sizes than current polytomous DCMs and thus prove useful in many small-scale testing scenarios. (shrink)

The Gompertz distribution is commonly used in biology for modeling fatigue and mortality. This paper studies a class of models proposed by Adham and Walker, featuring a Gompertz type distribution where the dependence structure is modeled by a lognormal distribution, and develops a new multivariate formulation that facilitates several numerical and computational aspects. This paper also implements the FBST, the Full Bayesian Significance Test for pertinent sharp (precise) hypotheses on the lognormal covariance structure. The FBST’s e-value, ev(H), gives (...) the epistemic value of hypothesis, H, or the value of evidence in the observed in support of H. (shrink)

In this article, we develop a hierarchical Bayesian model of learning in a general type of artificial language‐learning experiment in which learners are exposed to a mixture of grammars representing the variation present in real learners’ input, particularly at times of language change. The modeling goal is to formalize and quantify hypothesized learning biases. The test case is an experiment (Culbertson, Smolensky, & Legendre, 2012) targeting the learning of word‐order patterns in the nominal domain. The model identifies internal (...) biases of the experimental participants, providing evidence that learners impose (possibly arbitrary) properties on the grammars they learn, potentially resulting in the cross‐linguistic regularities known as typological universals. Learners exposed to mixtures of artificial grammars tended to shift those mixtures in certain ways rather than others; the model reveals how learners’ inferences are systematically affected by specific prior biases. These biases are in line with a typological generalization—Greenberg's Universal 18—which bans a particular word‐order pattern relating nouns, adjectives, and numerals. (shrink)

We show how the Full Bayesian Significance Test (FBST) can be used as a model selection criterion. The FBST was presented by Pereira and Stern as a coherent Bayesian significance test. Key Words: Bayesian test; Evidence; Global optimization; Information; Model selection; Numerical integration; Posterior density; Precise hypothesis; Regularization. AMS: 62A15; 62F15; 62H15.

In this article, epoch-based dynamic features such as sequence of epoch interval values and epoch strength values are explored to classify infant cries. Epoch is the instant of significant excitation of the vocal tract system during the production of speech. For voiced speech, the most significant excitation takes place around the instant of glottal closure. The different types of infant cries considered in this work are hunger, pain, and wet diaper. In this work, epoch strength and epoch interval features are (...) used to represent infant cry-specific information from the acoustic signal. In this study, the proposed features such as epoch interval and epoch strength values are determined using zero-frequency filter-based method. Gaussian mixture models are used to classify the above-mentioned cries from the features proposed in this work. GMMs are developed separately for each of the cries using the proposed features. The infant cry database collected under a telemedicine project at the Indian Institute of Technology Kharagpur has been used for this study. In the first step, infant cry recognition accuracy is investigated separately using epoch interval and epoch strength features. To enhance recognition performance, GMMs developed using various features are combined through score level fusion techniques. The recognition performance using a combination of evidence is found to be superior over individual systems. (shrink)

Statistical tests that detect and measure deviation from the Hardy-Weinberg equilibrium (HWE) have been devised but are limited when testing for deviation at multiallelic DNA loci is attempted. Here we present the full Bayesian significance test (FBST) for the HWE. This test depends neither on asymptotic results nor on the number of possible alleles for the particular locus being evaluated. The FBST is based on the computation of an evidence index in favor of the HWE hypothesis. A (...) great deal of forensic inference based on DNA evidence assumes that the HWE is valid for the genetic loci being used. We applied the FBST to genotypes obtained at several multiallelic short tandem repeat loci during routine parentage testing; the locus Penta E exemplifies those clearly in HWE while others such as D10S1214 and D19S253 do not appear to show this. (shrink)

Automatic incident detection plays a vital role among all the safety-critical applications under the parasol of Intelligent Transportation Systems to provide timely information to passengers and other stakeholders in smart cities. Moreover, accurate classification of these incidents with respect to type and severity assists the Traffic Incident Management Systems and stakeholders in devising better plans for incident site management and avoiding secondary incidents. Most of the AID systems presented in the literature are incident type-specific, i.e., either they are designed for (...) the detection of accident or congestion. While traveling along the road, one may come across different types of traffic incidents, such as accidents, congestion, and reckless driving. This necessitates that the AID system detects and classifies not only all the popular traffic incident types, but severity as well that is associated with these incidents. Therefore, this study aims to propose an efficient incident detection and classification framework for smart cities, by incorporating the efficacy of model stacking, to classify the incidents with respect to their types and severity levels. The experimental results showed that the proposed E-IDC framework achieved performance gains of 5%–56% in terms of incident severity classification and 1%–14% in terms of incident type classification when applied with different classifiers. We have also applied the Wilcoxon test to benchmark the performance of our proposed framework that reflects the significance of our approach over existing individual incident predictors in terms of severity and type classification. Moreover, it has been observed that the proposed E-IDC framework outperforms the existing ensemble technique, such as XGBoost used for the classification of incidents. (shrink)

The concept of non-arbitrage plays an essential role in finance theory. Under certain regularity conditions, the Fundamental Theorem of Asset Pricing states that, in non-arbitrage markets, prices of financial instruments are martingale processes. In this theoretical framework, the analysis of the statistical distributions of financial assets can assist in understanding how participants behave in the markets, and may or may not engender arbitrage conditions. Assuming an underlying Variance Gamma statistical model, this study aims to test, using the FBST - Full (...) Bayesian Significance Test, if there is a relevant price difference between essentially the same financial asset traded at two distinct locations. Specifically, we investigate and compare the behavior of call options on the BOVESPA Index traded at (a) the Equities Segment and (b) the Derivatives Segment of BM&FBovespa. Our results seem to point out significant statistical differences. To what extent this evidence is actually the expression of perennial arbitrage opportunities is still an open question. (shrink)

Decoupling is a general principle that allows us to separate simple components in a complex system. In statistics, decoupling is often expressed as independence, no association, or zero covariance relations. These relations are sharp statistical hypotheses, that can be tested using the FBST - Full Bayesian Significance Test. Decoupling relations can also be introduced by some techniques of Design of Statistical Experiments, DSEs, like randomization. This article discusses the concepts of decoupling, randomization and sparsely connected statistical models in (...) the epistemological framework of cognitive constructivism. (shrink)

Chow' efforts toward a methodology of theory-corroboration and the plea for significance testing are welcome, but there are many risky claims. A major omission is a discussion of significance testing in the Bayesian framework. We sketch here the Bayesian reinterpretation of the significance level for assessing direction of effects.

The Full Bayesian Significance Test, FBST, is extensively reviewed. Its test statistic, a genuine Bayesian measure of evidence, is discussed in detail. Its behavior in some problems of statistical inference like testing for independence in contingency tables is discussed.

The contributions to Testing Scientific Theories are unified by an in-terest in responding to criticisms directed by Glymour against existing models of confirmation—chiefly H-D and Bayesian schemas—and in assessing and correcting the "bootstrap" model of confirmation that he proposed as an alternative in Theory and Evidence (1980). As such, they provide a representative sample of objections to Glymour's model and of the wide range of new initiatives in thinking about scientific confirmation that it has influenced. The effect is a (...) sense of engagement and focus, and of significant advance at least in articulation of the problems that require solution; as Earman observes, "it is . . . heartening to report that the various opposing camps learned from each other" (p. vi).' In what follows I am concerned to assess what has been learned both critically, about Glymour's model, and constructively about the resources of the alternatives he challenges. My thesis is that while the theories emerging in this debate do deal with a wider range of scientific practice than before, their remaining limitations raise important questions about the ambitions and criteria of adequacy that have traditionally governed philosophical inquiry in this area. (shrink)

This paper develops computational models to monitor patients with hip replacement surgery. The Kinect camera is used to capture the movements of patients who are performing rehabilitation exercises with both lower limbs, specifically, ‘side step’ and ‘knee lift’ with each leg. The information is measured at 25 body points with their respective coordinates. Features selection algorithms are applied to the 75 attributes of the initial and final position vector of each rehab exercise. Different classification techniques have been tested and (...) class='Hi'>Bayesian networks, supervised classifier system and genetic algorithm with neural network have been selected and jointly applied to identify the correct and incorrect movements during the execution of the rehabilitation exercises. Besides, prediction models of the evolution of a patient are developed based on the average values of some motion related variables. These models can help to fasten the recovery of these patients. (shrink)

In classifications of vocabulary knowledge, vocabulary size and depth have often been separately conceptualized (Schmitt, 2014). Although size and depth are known to be substantially correlated, it is not clear whether they are a single construct or two separate components of vocabulary knowledge (Yanagisawa & Webb, 2020). This issue has not been addressed extensively in the literature and can be better examined using structural equation modeling (SEM), with measurement error modeled separately from the construct of interest. The current study reports (...) on conventional and Bayesian SEM approaches (e.g., Muthén & Asparouhov, 2012) to examine the factor structure of the size and depth of second language vocabulary knowledge of Japanese adult learners of English. A total of 255 participants took five vocabulary tests. One test was designed to measure vocabulary size in terms of the number of words known, while the remaining four were designed to measure vocabulary depth in terms of word association, polysemy, and collocation. All tests used a multiple-choice format. The size test was divided into three subtests according to word frequency. Results from conventional and Bayesian SEM show that a correlated two-factor model of size and depth with three and four indicators, respectively, fit better than a single-factor model of size and depth. In the two-factor model, vocabulary size and depth were strongly correlated (r =.945 for conventional SEM and.943 for Bayesian SEM with cross loadings), but they were distinct. The implications of these findings are discussed. (shrink)

The unit root problem plays a central role in empirical applications in the time series econometric literature. However, significance tests developed under the frequentist tradition present various conceptual problems that jeopardize the power of these tests, especially for small samples. Bayesian alternatives, although having interesting interpretations and being precisely defined, experience problems due to the fact that that the hypothesis of interest in this case is sharp or precise. The Bayesian significance test used in (...) this article, for the unit root hypothesis, is based solely on the posterior density function, without the need of imposing positive probabilities to sets of zero Lebesgue measure. Furthermore, it is conducted under strict observance of the likelihood principle. It was designed mainly for testing sharp null hypotheses and it is called FBST for Full Bayesian Significance Test. (shrink)

High-dimensional data analysis has become the most challenging task nowadays. Dimensionality reduction plays an important role here. It focuses on data features, which have proved their impact on accuracy, execution time, and space requirement. In this study, a dimensionality reduction method is proposed based on the convolution of input features. The experiments are carried out on minimal preprocessed nine benchmark datasets. Results show that the proposed method gives an average 38% feature reduction in the original dimensions. The algorithm accuracy is (...) tested using the decision tree, support vector machine, and K-nearest neighbor classifiers and evaluated with the existing principal component analysis algorithm. The average increase in accuracy is 8.06 for DT, 5.80 for SVM, and 18.80 for the KNN algorithm. The most significant characteristic feature of the proposed model is that it reduces attributes, leading to less computation time without loss in classifier accuracy. (shrink)

The paper aims to discuss the results of testing a neural network which classifies the vowels of the vocalic system with the [ATR] (Advanced Tongue Root) contrast based on the data of Akebu (Kwa family). The acoustic nature of the [ATR] feature is yet understudied. The only reliable acoustic correlate of [ATR] is the magnitude of the first formant (F1) which can be also modulated by tongue height, resulting in significant overlap between high [-ATR] vowels and mid [+ATR] vowels. Other (...) acoustic metrics which had been associated with the [ATR], such as F1 bandwidth (B1), relative intensity of F1 to F2 (A1-A2), etc., are typically inconsistent across vowel types and speakers. The values of four metrics – F1, F2, A1-A2, B1 – were used for training and testing the neural network. We tested four versions of the model differing in the presence of the fifth variable encoding the speaker and the number of hidden layers. The models which included the variable encoding the speaker achieved slightly higher accuracy, whereas the precision and recall metrics of the three-layer model were generally higher than those with two hidden layers. (shrink)

The paper aims to discuss the results of testing a neural network which classifies the vowels of the vocalic system with the [ATR] (Advanced Tongue Root) contrast based on the data of Akebu (Kwa family). The acoustic nature of the [ATR] feature is yet understudied. The only reliable acoustic correlate of [ATR] is the magnitude of the first formant (F1) which can be also modulated by tongue height, resulting in significant overlap between high [-ATR] vowels and mid [+ATR] vowels. Other (...) acoustic metrics which had been associated with the [ATR], such as F1 bandwidth (B1), relative intensity of F1 to F2 (A1-A2), etc., are typically inconsistent across vowel types and speakers. The values of four metrics – F1, F2, A1-A2, B1 – were used for training and testing the neural network. We tested four versions of the model differing in the presence of the fifth variable encoding the speaker and the number of hidden layers. The models which included the variable encoding the speaker achieved slightly higher accuracy, whereas the precision and recall metrics of the three-layer model were generally higher than those with two hidden layers. (shrink)

The accurate recognition of speech is beneficial to the fields of machine translation and intelligent human–computer interaction. After briefly introducing speech recognition algorithms, this study proposed to recognize speech with a recurrent neural network (RNN) and adopted the connectionist temporal classification (CTC) algorithm to align input speech sequences and output text sequences forcibly. Simulation experiments compared the RNN-CTC algorithm with the Gaussian mixture model–hidden Markov model and convolutional neural network-CTC algorithms. The results demonstrated that the more training samples the (...) speech recognition algorithm had, the higher the recognition accuracy of the trained algorithm was, but the training time consumption increased gradually; the more samples a trained speech recognition algorithm had to test, the lower the recognition accuracy and the longer the testing time. The proposed RNN-CTC speech recognition algorithm always had the highest accuracy and the lowest training and testing time among the three algorithms when the number of training and testing samples was the same. (shrink)

The vital data about the electrical activities of the brain are carried by the electroencephalography signals. The recordings of the electrical activity of brain neurons in a rhythmic and spontaneous manner from the scalp surface are measured by EEG. One of the most important aspects in the field of neuroscience and neural engineering is EEG signal analysis, as it aids significantly in dealing with the commercial applications as well. To uncover the highly useful information for neural classification activities, EEG studies (...) incorporated with machine learning provide good results. In this study, a Fusion Hybrid Model with Singular Value Decomposition Based Estimation of Robust Parameters is proposed for efficient feature extraction of the biosignals and to understand the essential information it has for analyzing the brain functionality. The essential features in terms of parameter components are extracted using the developed hybrid model, and a specialized hybrid swarm technique called Hybrid Differential Particle Artificial Bee algorithm is proposed for feature selection. To make the EEG more practical and to be used in a plethora of applications, the robust classification of these signals is necessary thereby relying less on the trained professionals. Therefore, the classification is done initially using the proposed Zero Inflated Poisson Mixture Regression Model and then it is also classified with a deep learning methodology, and the results are compared with other standard machine learning techniques. This proposed flow of methodology is validated on a few standard Biosignal datasets, and finally, a good classification accuracy of 98.79% is obtained for epileptic dataset and 98.35% is obtained for schizophrenia dataset. (shrink)

This manuscript constructs an intelligent sentiment analysis and marketing model for bed and breakfast consumption based on a behavioral psychology perspective. Based on the LDA theme model, the theme features and keywords of the reviews covering user feedback are explored from the text data, and the theme framework of user sentiment perception is constructed by combining previous literature on user perception in the B&B market, and the themes of user online reviews are summarized in four dimensions: practical, sensory, cognitive, and (...) emotional components of user experience. In this manuscript, GooSeeker software was selected for data crawling and ROST CM developed by Wuhan University was used for text processing. To improve the accuracy of text classification and improve the missing data, the online comment text is divided into sentences by symbols, and the text is divided into words based on sentences, and the spatial vector model and the text feature word weighting method of TF-IDF are used for vector representation, and the polynomial Bayesian classifier is called to identify the topics of sentences. The classical Theory of Planned Behavior was used to analyze the influencing factors of the willingness to consume experiential B&B tourism, and countermeasure suggestions for the development of B&B tourism were proposed based on the research findings In the empirical testing stage, a questionnaire on the willingness to consume experiential B&B tourism was designed, and web research was chosen to collect the data. SPSS20.0 was used to conduct reliability analysis, factor analysis, correlation analysis, and regression analysis on the data, and AMOS statistics were used to establish a structural equation model to verify the influence path of willingness to consume experiential B&B tourism. Finally, the moderating path of willingness to consume experiential B&B tourism was verified by using multi-group analysis. (shrink)

We review the definition of the Full Bayesian Significance Test (FBST), and summarize its main statistical and epistemological characteristics. We review also the Abstract Belief Calculus (ABC) of Darwiche and Ginsberg, and use it to analyze the FBST’s value of evidence. This analysis helps us understand the FBST properties and interpretation. The definition of value of evidence against a sharp hypothesis, in the FBST setup, was motivated by applications of Bayesian statistical reasoning to legal matters where the (...) sharp hypotheses were defendants statements, to be judged according to the Onus Probandi juridical principle. (shrink)

The origins of testing scientific models with statistical techniques go back to 18th century mathematics. However, the modern theory of statistical testing was primarily developed through the work of Sir R.A. Fisher, Jerzy Neyman, and Egon Pearson in the inter-war period. Some of Fisher's papers on testing were published in economics journals (Fisher, 1923, 1935) and exerted a notable influence on the discipline. The development of econometrics and the rise of quantitative economic models in the mid-20th century made statistical (...) class='Hi'>significance testing a commonplace, albeit controversial tool within economics. -/- In the debate about significance testing, methodological controversies intertwine with epistemological issues and sociological developments. Our aim in this chapter is to expound these connections and to show how the use of, and the debate about, significance testing in economics differs from other social sciences, such as psychology. (shrink)

How does personality influence the relationship between appraisals and emotions? Recent research suggests individual differences in appraisal structures: people may differ in an emotion's appraisal pattern. We explored individual differences in interest's appraisal structure, assessed as the within-person covariance of appraisals with interest. People viewed images of abstract visual art and provided ratings of interest and of interest's appraisals (novelty–complexity and coping potential) for each picture. A multilevel mixture model found two between-person classes that reflected distinct within-person appraisal styles. (...) For people in the larger class (68%), the novelty–complexity appraisal had a stronger effect on interest; for people in the smaller class (32%), the coping potential appraisal had a stronger effect. People in the larger class were significantly higher in appetitive traits related to novelty seeking (e.g., sensation seeking, openness to experience, and trait curiosity), suggesting that the appraisal classes have substantive meaning. We conclude by discussing the value of within-person mixture models for the study of personality and appraisal. (shrink)

Interactive sonification of biomechanical quantities is gaining relevance as a motor learning aid in movement rehabilitation, as well as a monitoring tool. However, existing gaps in sonification research have prevented its widespread recognition and adoption in such applications. The incorporation of embodied principles and musical structures in sonification design has gradually become popular, particularly in applications related to human movement. In this study, we propose a general sonification model for the sit-to-stand transfer, an important activity of daily living. The model (...) contains a fixed component independent of the use-case, which represents the rising motion of the body as an ascending melody using the physical model of a flute. In addition, a flexible component concurrently sonifies STS features of clinical interest in a particular rehabilitative/monitoring situation. Here, we chose to represent shank angular jerk and movement stoppages, through perceptually salient pitch modulations and bell sounds. We outline the details of our technical implementation of the model. We evaluated the model by means of a listening test experiment with 25 healthy participants, who were asked to identify six normal and simulated impaired STS patterns from sonified versions containing various combinations of the constituent mappings of the model. Overall, we found that the participants were able to classify the patterns accurately, confidently, and in a timely manner. The amount of sonified kinematic information significantly impacted classification accuracy. The six STS patterns were also classified with significantly different accuracy depending on their kinematic characteristics. Learning effects were seen in the form of increased accuracy and confidence with repeated exposure to the sound sequences. We found no significant accuracy differences based on the participants' level of music training. Overall, we see our model as a concrete conceptual and technical starting point for STS sonification design catering to rehabilitative and clinical monitoring applications. (shrink)

_Purpose:_ In order to develop an evidence-based, sport-specific minimum impairment criteria (MIC) for the sport of vision-impaired (VI) shooting, this study aimed to determine the relative influence of losses in visual acuity (VA) and contrast sensitivity (CS) on shooting performance. Presently, VA but not CS is used to determine eligibility to compete in VI shooting. _Methods:_ Elite able-sighted athletes ( n = 27) shot under standard conditions with their habitual vision, and with their vision impaired by the use of simulation (...) spectacles (filters which reduce both VA and CS) and refractive blur (lenses which reduce VA with less effect on CS). Habitual shooting scores were used to establish a cut-off in order to determine when shooting performance was ‘below expected’ in the presence of vision impairment. Logistic regression and decision tree analyses were then used to assess the relationship between visual function and shooting performance. _Results:_ Mild reductions in VA and/or CS did not alter shooting performance, with greater reductions required for shooting performance to fall below habitual levels (below 87% of normalized performance). Stepwise logistic regression selected CS as the most significant predictor of shooting performance, with VA subsequently improving the validity of the model. In an unconstrained decision tree analysis, CS was selected as the sole criterion (80%) for predicting ‘below expected’ shooting score. _Conclusion:_ Shooting performance is better predicted by losses in CS than by VA. Given that it is not presently tested during classification, the results suggest that CS is an important measure to include in testing for the classification of vision impairment for athletes competing in VI shooting. (shrink)

In this article, a highly effective Bayesian sampling algorithm based on auxiliary variables is proposed to analyze aberrant response and response time data. The new algorithm not only avoids the calculation of multidimensional integrals by the marginal maximum likelihood method but also overcomes the dependence of the traditional Metropolis–Hastings algorithm on the tuning parameter in terms of acceptance probability. A simulation study shows that the new algorithm is accurate for parameter estimation under simulation conditions with different numbers of examinees, (...) items, and speededness levels. Based on the sampling results, the powers of the two proposed Bayesian assessment criteria are tested in the simulation study. Finally, a detailed analysis of a high-state and large-scale computerized adaptive test dataset is carried out to illustrate the proposed methodology. (shrink)

A pure significance test would check the agreement of a statistical model with the observed data even when no alternative model was available. The paper proposes the use of a modified p -value to make such a test. The model will be rejected if something surprising is observed. It is shown that the relation between this measure of surprise and the surprise indices of Weaver and Good is similar to the relationship between a p -value, a corresponding odds-ratio, and (...) a logit or log-odds statistic. The s -value is always larger than the corresponding p -value, and is not uniformly distributed. Difficulties with the whole approach are discussed. (shrink)

Automatic moving object detection and tracking is very important task in video surveillance applications. In the present work the well known background subtraction model and use of Gaussian Mixture Models have been used to implement a robust automated single object tracking system. In this implementation, background subtraction on subtracting consecutive frame-by-frame basis for moving object detection is done. Once the object has been detected it is tracked by employing an efficient GMM technique. After successful completion of tracking, moving object (...) recognition of those objects using well known Principal Component Analysis, which is used for extracting features and Manhattan based distance metric is used for subsequent classification purpose. The system is capable of handling entry and exit of an object. Such a tracking system is cost effective and can be used as an automated video conferencing system and also has applications like human tracking, vehicles monitoring, and event recognition for video surveillance. The proposed algorithm was tested on standard database on complex environments and the results were satisfactory. (shrink)

The objective of this study was to examine the association between resilience and trajectories of college students’ negative and positive affect during the COVID-19 pandemic. A total of 391 college students recruited from China completed a daily online negative and positive affect scale for 1 week, and their resilience was also measured. Profiles of brief trajectories of negative and positive affect over time were identified using the latent growth mixture model, and the effect of resilience on these trajectories was (...) further explored. Two latent profiles of negative affect were found: a constant high negative affect profile and a slowly decreasing low negative affect profile, while three latent profiles of positive affect were identified: a slowly increasing high positive affect profile, a rapidly decreasing medium positive affect profile, and a constant medium positive affect profile. The optimism dimension of resilience predicted the membership in the various profiles significantly, whereas the prediction of tenacity and strength dimensions of resilience was not significant. Activities that promote resilience, especially optimism, should be included to improve the daily emotions of college students during COVID-19. (shrink)

This article introduces a new hybrid intake procedure developed for posttraumatic stress disorder (PTSD) screening, which combines an automated textual assessment of respondents’ self-narratives and item-based measures that are administered consequently. Text mining technique and item response modeling were used to analyze long constructed response (i.e., self-narratives) and responses to standardized questionnaires (i.e., multiple choices), respectively. The whole procedure is combined in a Bayesian framework where the textual assessment functions as prior information for the estimation of the PTSD latent (...) trait. The purpose of this study is twofold: first, to investigate whether the combination model of textual analysis and item-based scaling could enhance the classification accuracy of PTSD, and second, to examine whether the standard error of estimates could be reduced through the use of the narrative as a sort of routing test. With the sample at hand, the combination model resulted in a reduction in the misclassification rate, as well as a decrease of standard error of latent trait estimation. These findings highlight the benefits of combining textual assessment and item-based measures in a psychiatric screening process. We conclude that the hybrid test design is a promising approach to increase test efficiency and is expected to be applicable in a broader scope of educational and psychological measurement in the future. (shrink)

In this paper, the notion of degree of inconsistency is introduced as a tool to evaluate the sensitivity of the Full Bayesian Significance Test (FBST) value of evidence with respect to changes in the prior or reference density. For that, both the definition of the FBST, a possibilistic approach to hypothesis testing based on Bayesian probability procedures, and the use of bilattice structures, as introduced by Ginsberg and Fitting, in paraconsistent logics, are reviewed. The computational and theoretical (...) advantages of using the proposed degree of inconsistency based sensitivity evaluation as an alternative to traditional statistical power analysis is also discussed. (shrink)

The Gibbs sampler can be used to obtain samples of arbitrary size from the posterior distribution over the parameters of a structural equation model (SEM) given covariance data and a prior distribution over the parameters. Point estimates, standard deviations and interval estimates for the parameters can be computed from these samples. If the prior distribution over the parameters is uninformative, the posterior is proportional to the likelihood, and asymptotically the inferences based on the Gibbs sample are the same as those (...) based on the maximum likelihood solution, e.g., output from LISREL or EQS. In small samples, however, the likelihood surface is not Gaussian and in some cases contains local maxima. Nevertheless, the Gibbs sample comes from the correct posterior distribution over the parameters regardless of the sample size and the shape of the likelihood surface. With an informative prior distribution over the parameters, the posterior can be used to make inferences about the parameters of underidentified models, as we illustrate on a simple errors-in-variables model. (shrink)

The capacity to generate recursive sequences is a marker of rich, algorithmic cognition, and perhaps unique to humans. Yet, the precise processes driving recursive sequence generation remain mysterious. We investigated three potential cognitive mechanisms underlying recursive pattern processing: hierarchical reasoning, ordinal reasoning, and associative chaining. We developed a Bayesian mixture model to quantify the extent to which these three cognitive mechanisms contribute to adult humans’ performance in a sequence generation task. We further tested whether recursive rule discovery depends (...) upon relational information, either perceptual or semantic. We found that the presence of relational information facilitates hierarchical reasoning and drives the generation of recursive sequences across novel depths of center embedding. In the absence of relational information, the use of ordinal reasoning predominates. Our results suggest that hierarchical reasoning is an important cognitive mechanism underlying recursive pattern processing and can be deployed across embedding depths and relational domains. (shrink)

The crisis of replication has led many to blame statistical significance tests for making it too easy to find impressive looking effects that do not replicate. However, the very fact it becomes difficult to replicate effects when features of the tests are tied down actually serves to vindicate statistical significance tests. While statistical significance tests, used correctly, serve to bound the probabilities of erroneous interpretations of data, this error control is nullified by data-dredging, (...) multiple testing, and other biasing selection effects. Arguments claiming to vitiate statistical significance tests attack straw person variants of tests that commit well-known fallacies and misinterpretations. There is a tension between popular calls for preregistration – arguably, one of the most promising ways to boost replication – and accounts that downplay error probabilities: Bayes Factors, Bayesian posteriors, likelihood ratios. By underscoring the importance of error control for well testedness, the replication crisis points to reformulating tests so as to avoid fallacies and report the extent of discrepancies that are and are not indicated with severity. (shrink)

Much forensic inference based upon DNA evidence is made assuming Hardy-Weinberg Equilibrium (HWE) for the genetic loci being used. Several statistical tests to detect and measure deviation from HWE have been devised, and their limitations become more obvious when testing for deviation within multiallelic DNA loci. The most popular methods-Chi-square and Likelihood-ratio tests-are based on asymptotic results and cannot guarantee a good performance in the presence of low frequency genotypes. Since the parameter space dimension increases at a quadratic (...) rate on the number of alleles, some authors suggest applying sequential methods, where the multiallelic case is reformulated as a sequence of “biallelic” tests. However, in this approach it is not obvious how to assess the general evidence of the original hypothesis; nor is it clear how to establish the significance level for its acceptance/rejection. In this work, we introduce a straightforward method for the multiallelic HWE test, which overcomes the aforementioned issues of sequential methods. The core theory for the proposed method is given by the Full Bayesian Significance Test (FBST), an intuitive Bayesian approach which does not assign positive probabilities to zero measure sets when testing sharp hypotheses. We compare FBST performance to Chi-square, Likelihood-ratio and Markov chain tests, in three numerical experiments. The results suggest that FBST is a robust and high performance method for the HWE test, even in the presence of several alleles and small sample sizes. (shrink)

Frequentism and Bayesianism represent very different approaches to hypothesis testing, and this presents a skeptical challenge for Bayesians. Given that most empirical research uses frequentist methods, why (if at all) should we rely on it? While it is well known that there are conditions under which Bayesian and frequentist methods agree, without some reason to think these conditions are typically met, the Bayesian hasn’t shown why we are usually safe in relying on results reported by significance testers. (...) In this article, I provide arguments that such conditions will usually be met; the Bayesian can maintain her theoretical disagreement with the frequentist while holding that her error is mostly harmless in practice. (shrink)

Objective: Based on the theory of perceived organizational support (POS), conservation of resource (COR) and job demands-resources (JD-R) model, this study establishes a moderated mediation model to test the role of job satisfaction in mediating the relationship between perceived organizational support and job burnout, as well as the role of regulatory emotional self-efficacy in moderating the above mediating process. Method: A total of 784 police officers were surveyed with the Perceived Organizational Support Scale, the Job Burnout Questionnaire, the Regulatory Emotional (...) Self-Efficacy Scale, and the Minnesota Job Satisfaction Questionnaire. Results: (1) After controlling for gender, seniority, age, police classification, education, and marital status, regression analysis showed a significant negative correlation between perceived organizational support and burnout (r = -0.42, p < 0.01), and the former had a significant negative predictive effect on job burnout (β = -0.42, p < 0.001). (2) The mediating effect test shows that job satisfaction plays a partial role in mediating the relationship between perceived organizational support and job burnout. (3) Through the analysis of the moderated mediation model test, regulatory emotional self-efficacy moderates the first half of the path of “perceived organizational support → job satisfaction → job burnout". Conclusion: Perceived organizational support not only directly affects police job burnout but also indirectly affects police job burnout through job satisfaction. Regulatory emotional self-efficacy enhances the influence of organizational support on job satisfaction. This study indicates the combined effect of perceived organizational support, job satisfaction and regulatory emotional self-efficacy on job burnout and has certain guiding significance for alleviating police job burnout. Keywords: Perceived organizational support, Job burnout, Job satisfaction, Regulatory emotional self-efficacy, Moderated mediation. (shrink)

The Generalized Poisson Distribution (GPD) adds an extra parameter to the usual Poisson distribution. This parameter induces a loss of homogeneity in the stochastic processes modeled by the distribution. Thus, the generalized distribution becomes an useful model for counting processes where the occurrence of events is not homogeneous. This model creates the need for an inferential procedure, to test for the value of this extra parameter. The FBST (Full Bayesian Significance Test) is a Bayesian hypotheses test procedure, (...) capable of providing an evidence measure on sharp hypotheses (where the dimension of the parametric space under the null hypotheses is smaller than that of the full parametric space). The goal of this work is study the empirical properties of the FBST for testing the nullity of extra parameter of the generalized Poisson distribution. Numerical experiments show a better performance of FBST with respect to the classical likelihood ratio test, and suggest that FBST is an efficient and robust tool for this application. (shrink)