Search

Pipe breaks are a recurrent problem in water distribution networks and detecting them quickly is crucial to minimize the economic and environmental costs for municipalities. This study presents a burst detection methodology applying Bayesian dynamic linear models (DLMs) on water flow time series combined with an outlier monitoring tool. The model is used to characterize the actual flow and, for each time, a one‐step ahead forecast distribution is obtained recursively before moving onto the next observation. The outlier detection method consists of comparing the main model with an alternative one wherein the mean flow is shifted to a higher value (as bursts tend to increase flow) to evaluate which model best fit the observed data. If the alternative model is favored, a burst alarm is issued. To verify the performance of this approach, the DLM and monitoring tool were applied on 2 yr of flow data from two district meter areas (DMAs) in Halifax (Canada), and a historical break data set is used to assess model accuracy. The model was able to detect up to 75% and 71.2% of the pipe breaks, with a false alarm rate of 5.15% and 12% in the first and second DMA, respectively. Finally, the proposed model allows for straightforward interpretation of model parameters, nonlinear relationship between flow and predictors of interest, naturally describes the uncertainty for future predictions, can easily accommodate missing values and can be tuned to maximize break detection or minimize false alarm rates to adapt to specific objectives of water infrastructure managers. Key Points: A Bayesian dynamic linear model is developed to detect pipe burst by modeling flow time series and monitoring outliersThe model naturally accommodates nonlinear associations between flow and predictors (e.g., pressure, temperature)Non‐stationarity of the flow time series is naturally accounted for through the dynamic structure of the parameters in the model [ABSTRACT FROM AUTHOR]

Over the past decades, powerful MRI‐based methods have been developed, which yield both voxel‐based maps of the brain activity and anatomical variation related to different conditions. With regard to functional or structural MRI data, forward inferences try to determine which areas are involved given a mental function or a brain disorder. A major drawback of forward inference is its lack of specificity, as it suggests the involvement of brain areas that are not specific for the process/condition under investigation. Therefore, a different approach is needed to determine to what extent a given pattern of cerebral activation or alteration is specifically associated with a mental function or brain pathology. In this study, we present a new tool called BACON (Bayes fACtor mOdeliNg) for performing reverse inference both with functional and structural neuroimaging data. BACON implements the Bayes' factor and uses the activation likelihood estimation derived‐maps to obtain posterior probability distributions on the evidence of specificity with regard to a particular mental function or brain pathology. [ABSTRACT FROM AUTHOR]

In the field of neuroimaging reverse inferences can lead us to suppose the involvement of cognitive processes from certain patterns of brain activity. However, the same reasoning holds if we substitute "brain activity" with "brain alteration" and "cognitive process" with "brain disorder." The fact that different brain disorders exhibit a high degree of overlap in their patterns of structural alterations makes forward inference‐based analyses less suitable for identifying brain areas whose alteration is specific to a certain pathology. In the forward inference‐based analyses, in fact, it is impossible to distinguish between areas that are altered by the majority of brain disorders and areas that are specifically affected by certain diseases. To address this issue and allow the identification of highly pathology‐specific altered areas we used the Bayes' factor technique, which was employed, as a proof of concept, on voxel‐based morphometry data of schizophrenia and Alzheimer's disease. This technique allows to calculate the ratio between the likelihoods of two alternative hypotheses (in our case, that the alteration of the voxel is specific for the brain disorder under scrutiny or that the alteration is not specific). We then performed temporal simulations of the alterations' spread associated with different pathologies. The Bayes' factor values calculated on these simulated data were able to reveal that the areas, which are more specific to a certain disease, are also the ones to be early altered. This study puts forward a new analytical instrument capable of innovating the methodological approach to the investigation of brain pathology. [ABSTRACT FROM AUTHOR]

The completion of the third molar roots has played an important role in ascertaining whether individuals may be at or over a legal threshold of age, often taken as 18 years. This study demonstrates that root apex completion in the third molar is relatively uninformative regarding the threshold of age 18 years in a sample of 1184 males, where mean age‐of‐attainment of root apex completion for third mandibular molars is about 19.4 years. This paper also considers the legal age threshold problem for cases where the third mandibular molar is not completely formed, and outlines the use of parametric models and Bayes' factors to evaluate dental evidence in statistically appropriate ways. It attempts to resolve confusion over age‐within‐stage versus age‐of‐attainment, likelihood ratios versus other diagnostic tests, and prior odds for a case versus the prior density for an age distribution. [ABSTRACT FROM AUTHOR]

False positive findings in science are inevitable, but are they particularly common in psychology and psychiatry? The evidence that we review suggests that while not restricted to our field, the problem is acute. We describe the concept of researcher ‘degrees-of-freedom’ to explain how many false-positive findings arise, and how the various strategies of registration, pre-specification, and reporting standards that are being adopted both reduce and make these visible. We review possible benefits and harms of proposed statistical solutions, from tougher requirements for significance, to Bayesian and machine learning approaches to analysis. Finally we consider the organisation and methods for replication and systematic review in psychology and psychiatry. [ABSTRACT FROM AUTHOR]

While model evidence is considered by Bayesian statisticians as a gold standard for model selection (the ratio in model evidence between two models giving the Bayes factor), its calculation is often viewed as too computationally demanding for many applications. By contrast, the widely used deviance information criterion (DIC), a different measure that balances model accuracy against complexity, is commonly considered a much faster alternative. However, recent advances in computational tools for efficient multi-temperature Markov chain Monte Carlo algorithms, such as steppingstone sampling (SS) and thermodynamic integration schemes, enable efficient calculation of the Bayesian model evidence. This paper compares both the capability (i.e. ability to select the true model) and speed (i.e. CPU time to achieve a given accuracy) of DIC with model evidence calculated using SS. Three important model classes are considered: linear regression models, mixed models and compartmental models widely used in epidemiology. While DIC was found to correctly identify the true model when applied to linear regression models, it led to incorrect model choice in the other two cases. On the other hand, model evidence led to correct model choice in all cases considered. Importantly, and perhaps surprisingly, DIC and model evidence were found to run at similar computational speeds, a result reinforced by analytically derived expressions.

Structural health monitoring (SHM) and non-destructive testing (NDT) provide necessary information in assessing the current condition of an ageing structure. However, measurements obtained from SHM and NDT can be erroneous and it is important that the uncertainties associated with these measurement errors are quantified through instrument calibration. Considering that the process of calibration is expensive and time consuming, a novel optimum instrument calibration methodology is proposed in this work, which uses an information theoretic criterion to decide on the optimum number of calibration data points required. The proposed methodology is demonstrated through the calibration of a corrosion rate instrument with measurement uncertainty. The calibrated model is validated using Bayes' factor as the validation metric. Case studies demonstrate 42.5% and more reduction in the required number of calibration data points. The proposed method also suggests an optimum sequence of data collection for calibration. • A novel optimum NDT/SHM instrument calibration method is proposed. • The proposed method uses information theoretic criterion in a Bayesian framework. • This is illustrated through the optimum calibration of a corrosion rate instrument. • Validation of the proposed calibration is demonstrated using Bayes' factor. [ABSTRACT FROM AUTHOR]

The introduction of shape parameters into statistical distributions provided flexible models that produced better fit to experimental data. The Weibull and gamma families are prime examples wherein shape parameters produce more reliable statistical models than standard exponential models in lifetime studies. In the presence of many independent gamma populations, one may test equality (or homogeneity) of shape parameters. In this article, we develop two tests for testing shape parameters of gamma distributions using chi-square distributions, stochastic majorization, and Schur convexity. The first one tests hypotheses on the shape parameter of a single gamma distribution. We numerically examine the performance of this test and find that it controls Type I error rate for small samples. To compare shape parameters of a set of independent gamma populations, we develop a test that is unbiased in the sense of Schur convexity. These tests are motivated by the need to have simple, easy to use tests and accurate procedures in case of small samples. We illustrate the new tests using three real datasets taken from engineering and environmental science. In addition, we investigate the Bayes’ factor in this context and conclude that for small samples, the frequentist approach performs better than the Bayesian approach. [ABSTRACT FROM AUTHOR]

Additive manufacturing has enabled the construction of increasingly complex mechanical structures. However, the variability of mechanical properties may be higher than that of conventionally manufactured structures. Typically, the computational cost of the numerical modeling of such structures considerably increases when variability is considered. In deterministic analyses of periodic structures, the dispersion diagrams obtained for the first Brillouin zone (FBZ) can be used to predict attenuation bands for any direction of propagation. This can be further simplified considering only the contour of the irreducible Brillouin zone (IBZ) if the unit cell presents symmetries. The objective of the current investigation is to present evidence that, similarly to what occurs in deterministic cases, the stochastic results obtained by scanning only the IBZ contour of the proposed two-dimensional unit cell under 4-fold rotational symmetry of statistical variability coincides with statistical results obtained scanning the FBZ. This is not a direct result, because each individual unit cell sample is asymmetric. We show that, under symmetry of variability statistics, the stochastic results computed for supercells and for finite metastructures consisting of a finite number of cells also coincide with the results for the IBZ contour of the unit cell. This result is important, as it dramatically reduces the computation cost of the stochastic analysis of such structures. • Under symmetry of variability, the IBZ coincides results obtained scanning the FBZ. • Stochastic results of supercells and finite metastructures coincide with the IBZ. • The results show a way to reduce the computation cost of the stochastic analysis. [ABSTRACT FROM AUTHOR]

This study presents a classification criteria for two-class Cannabis seedlings. As the cultivation of drug type cannabis is forbidden in Switzerland, law enforcement authorities regularly ask laboratories to determine cannabis plant's chemotype from seized material in order to ascertain that the plantation is legal or not. In this study, the classification analysis is based on data obtained from the relative proportion of three major leaf compounds measured by gas-chromatography interfaced with mass spectrometry (GC-MS). The aim is to discriminate between drug type (illegal) and fiber type (legal) cannabis at an early stage of the growth. A Bayesian procedure is proposed: a Bayes factor is computed and classification is performed on the basis of the decision maker specifications (i.e. prior probability distributions on cannabis type and consequences of classification measured by losses). Classification rates are computed with two statistical models and results are compared. Sensitivity analysis is then performed to analyze the robustness of classification criteria. [ABSTRACT FROM AUTHOR]

This paper extends previous research [1] on the use of multivariate continuous data in comparative handwriting examinations, notably for gender classification. A database has been constructed by analyzing the contour shape of loop characters of type a and d by means of Fourier analysis, which allows characters to be described in a global way by a set of variables (e.g., Fourier descriptors). Sample handwritings were collected from right- and left-handed female and male writers. The results reported in this paper provide further arguments in support of the view that investigative settings in forensic science represent an area of application for which the Bayesian approach offers a logical framework. In particular, the Bayes factor is computed for settings that focus on inference of gender and handedness of the author of an incriminated handwritten text. An emphasis is placed on comparing the efficiency for investigative purposes of characters a and d. [ABSTRACT FROM AUTHOR]

There is a rich literature on Bayesian variable selection for parametric models. Our focus is on generalizing methods and asymptotic theory established for mixtures ofg-priors to semiparametric linear regression models having unknown residual densities. Using a Dirichlet process location mixture for the residual density, we propose a semiparametricg-prior which incorporates an unknown matrix of cluster allocation indicators. For this class of priors, posterior computation can proceed via a straightforward stochastic search variable selection algorithm. In addition, Bayes’ factor and variable selection consistency is shown to result under a class of proper priors ongeven when the number of candidate predictorspis allowed to increase much faster than sample sizen, while making sparsity assumptions on the true model size. [ABSTRACT FROM AUTHOR]

Abstract: Assessment of forensic findings with likelihood ratios is for several cases straightforward, but there are a number of situations where contemplation of the alternative explanation to the evidence needs consideration, in particular when it comes to the reporting of the evidentiary strength. The likelihood ratio approach cannot be directly applied to cases where the proposition alternative to the forwarded one is a set of multiple propositions with different likelihoods and different prior probabilities. Here we present a general framework based on the Bayes'' factor as the quantitative measure of evidentiary strength from which it can be deduced whether the direct application of a likelihood ratio is reasonable or not. The framework is applied on DNA evidence in forms of an extension to previously published work. With the help of a scale of conclusions we provide a solution to the problem of communicating to the court the evidentiary strength of a DNA match when a close relative to the suspect has a non-negligible prior probability of being the source of the DNA. [Copyright &y& Elsevier]

Abstract: This paper extends previous research and discussion on the use of multivariate continuous data, which are about to become more prevalent in forensic science. As an illustrative example, attention is drawn here on the area of comparative handwriting examinations. Multivariate continuous data can be obtained in this field by analysing the contour shape of loop characters through Fourier analysis. This methodology, based on existing research in this area, allows one describe in detail the morphology of character contours throughout a set of variables. This paper uses data collected from female and male writers to conduct a comparative analysis of likelihood ratio based evidence assessment procedures in both, evaluative and investigative proceedings. While the use of likelihood ratios in the former situation is now rather well established (typically, in order to discriminate between propositions of authorship of a given individual versus another, unknown individual), focus on the investigative setting still remains rather beyond considerations in practice. This paper seeks to highlight that investigative settings, too, can represent an area of application for which the likelihood ratio can offer a logical support. As an example, the inference of gender of the writer of an incriminated handwritten text is forwarded, analysed and discussed in this paper. The more general viewpoint according to which likelihood ratio analyses can be helpful for investigative proceedings is supported here through various simulations. These offer a characterisation of the robustness of the proposed likelihood ratio methodology. [Copyright &y& Elsevier]

There is a widespread recognition of the need for better information sharing and provision to improve the viability of end-of-life (EOL) product recovery operations. The emergence of automated data capture and sharing technologies such as RFID, sensors and networked databases has enhanced the ability to make product information; available to recoverers, which will help them make better decisions regarding the choice of recovery option for EOL products. However, these technologies come with a cost attached to it, and hence the question 'what is its value?' is critical. This paper presents a probabilistic approach to model product recovery decisions and extends the concept of Bayes' factor for quantifying the impact of product information on the effectiveness of these decisions. Further, we provide a quantitative examination of the factors that influence the value of product information, this value depends on three factors: (i) penalties for Type I and Type II errors of judgement regarding product quality; (ii) prevalent uncertainty regarding product quality and (iii) the strength of the information to support/contradict the belief. Furthermore, we show that information is not valuable under all circumstances and derive conditions for achieving a positive value of information. [ABSTRACT FROM AUTHOR]

This article is concerned with variable selection for cluster analysis. The problem is regarded as a model selection problem in the model-based cluster analysis context. A model generalizing the model of Raftery and Dean (2006, Journal of the American Statistical Association 101, 168–178) is proposed to specify the role of each variable. This model does not need any prior assumptions about the linear link between the selected and discarded variables. Models are compared with Bayesian information criterion. Variable role is obtained through an algorithm embedding two backward stepwise algorithms for variable selection for clustering and linear regression. The model identifiability is established and the consistency of the resulting criterion is proved under regularity conditions. Numerical experiments on simulated datasets and a genomic application highlight the interest of the procedure. [ABSTRACT FROM AUTHOR]

The formulae for computing the so-called Sib Index using codominant alleles for (1) full-sib and (2) half-sib parentage are given. Hypothesis testing is based on the distribution of conditional likelihood ratio or Bayes' factor. Thresholds for rejecting the null hypothesis and P -values were obtained in function of the number of alleles and their frequency distributions. Simulations showed that a relatively low number of marker systems (e.g. 20) are enough to accept the hypothesis of sib parentage with a reasonable power for usual significance levels, but that a higher number would be necessary if full-sib against half-sib parentage is the contrast to be carried out. The effect of sampling variation on the allele frequencies on power calculations is also analysed. [ABSTRACT FROM AUTHOR]

Forensic handwriting examiners (FHE) activities are focused on comparative analysis of handwritten objects such as signatures. Their role is to provide and evaluate evidence for and against the authenticity of a questioned signature. In recent years, cases involving handwritten signatures captured on electronic devices have become more commonplace. These so-called 'dynamic signatures' (also known as 'digitally captured signatures') are much different from paper-based signatures. Not only does the medium of recording differ, but also the type, volume of data and features are different from the pattern-based evidence that makes up paper-based signatures. Recent developments in forensic science - including signature examination - have led to the adoption of evaluative probabilistic methodologies in many disciplines [see, e.g. ENFSI 1915 Guidelines]. In the current paper, a probabilistic model to evaluate signature evidence in the form of multivariate data, as proposed and described in Wacom Europe GmbH (2019), is adopted. Topics like data sparsity, joint evaluation of multiple features and feature selection are investigated. Performed experimental studies showed an accuracy rate above 90% even when a limited number (5) of reference signatures was available. The performances of a multivariate approach are compared with those characterizing a so-called multiplicative approach where variables (features) are taken as independent and the Bayes' factor (BF) is obtained as the product of univariate BFs associated to each selected feature. The simplicity of this latter approach is, however, accompanied by severe issues about the reliability of results. The use of a multivariate approach is therefore highly recommended. Finally, the evidential values in correspondence of alternative feature sets are compared. Results suggest that discriminative features are writer-related and necessitate a case-specific selection. [ABSTRACT FROM AUTHOR]

Salivary microbiota profiles may represent a valid contribution to forensic investigation when standard DNA genotyping methods fail. Starting from questioned and control materials in the form of saliva, the evidence can be expressed by means of a distance between those materials taking into account specific aspects of the microbiota composition. The value of the evidence for forensic discrimination purposes is quantified by means of a Bayes' factor, that allows one to overcome the major limitations and pitfalls of intuition connected to the use of cut-off values as a mean of decision. • salivary microbiota composition to support discrimination between related individuals. • Beta-diversity indeces to highlight taxonomical differences between pairs of individuals. • a probabilistic approach to evaluate salivary microbiome. [ABSTRACT FROM AUTHOR]

Salivary microbiota profiles may represent a valid contribution to forensic investigation when standard DNA genotyping methods fail. Starting from questioned and control materials in the form of saliva, the evidence can be expressed by means of a distance between those materials taking into account specific aspects of the microbiota composition. The value of the evidence for forensic discrimination purposes is quantified by means of a Bayes' factor, that allows one to overcome the major limitations and pitfalls of intuition connected to the use of cut-off values as a mean of decision., (Copyright © 2021 Elsevier B.V. All rights reserved.)

Dynamic signatures are recordings of signatures made on digitizing devices such as tablet PCs. These handwritten signatures contain both dynamic and spatial information on every data point collected during the signature movement and can therefore be described in the form of multivariate data. The management of dynamic signatures represents a challenge for the forensic science community through its novelty and the volume of data available. Much as for static signatures, the authenticity of dynamic signatures may be doubted, which leads to a forensic examination of the unknown source signature. The Bayes' factor, as measure of evidential support, can be assigned with statistical models to discriminate between competing propositions. In this respect, the limitations of existing probabilistic solutions to deal with dynamic signature evidence is pointed out and explained in detail. In particular, the necessity to remove the independence assumption between questioned and reference material is emphasized. [ABSTRACT FROM AUTHOR]

The forensic science community has increasingly sought quantitative methods for conveying the weight of evidence. Experts from many forensic laboratories summarize their findings in terms of a likelihood ratio. Several proponents of this approach have argued that Bayesian reasoning proves it to be normative. We find this likelihood ratio paradigm to be unsupported by arguments of Bayesian decision theory, which applies only to personal decision making and not to the transfer of information from an expert to a separate decision maker. We further argue that decision theory does not exempt the presentation of a likelihood ratio from uncertainty characterization, which is required to assess the fitness for purpose of any transferred quantity. We propose the concept of a lattice of assumptions leading to an uncertainty pyramid as a framework for assessing the uncertainty in an evaluation of a likelihood ratio. We demonstrate the use of these concepts with illustrative examples regarding the refractive index of glass and automated comparison scores for fingerprints.

Diseases often cooccur in individuals more often than expected by chance, and may be explained by shared underlying genetic etiology. A common approach to genetic overlap analyses is to use summary genome-wide association study data to identify single-nucleotide polymorphisms (SNPs) that are associated with multiple traits at a selected P-value threshold. However, P-values do not account for differences in power, whereas Bayes' factors (BFs) do, and may be approximated using summary statistics. We use simulation studies to compare the power of frequentist and Bayesian approaches with overlap analyses, and to decide on appropriate thresholds for comparison between the two methods. It is empirically illustrated that BFs have the advantage over P-values of a decreasing type I error rate as study size increases for single-disease associations. Consequently, the overlap analysis of traits from different-sized studies encounters issues in fair P-value threshold selection, whereas BFs are adjusted automatically. Extensive simulations show that Bayesian overlap analyses tend to have higher power than those that assess association strength with P-values, particularly in low-power scenarios. Calibration tables between BFs and P-values are provided for a range of sample sizes, as well as an approximation approach for sample sizes that are not in the calibration table. Although P-values are sometimes thought more intuitive, these tables assist in removing the opaqueness of Bayesian thresholds and may also be used in the selection of a BF threshold to meet a certain type I error rate. An application of our methods is used to identify variants associated with both obesity and osteoarthritis., (© 2015 The Authors. *Genetic Epidemiology published by Wiley Periodicals, Inc.)