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According to van Fraassen, inference to the best explanation (IBE) is incompatible with Bayesianism. To argue to the contrary, many philosophers have suggested hybrid models of scientific reasoning with both explanationist and probabilistic elements. This paper offers another such model with two novel features. First, its Bayesian component is imprecise. Second, the domain of credence functions can be extended., Author(s): Namjoong Kim [sup.1] Author Affiliations: (1) grid.256681.e, 0000 0001 0661 1492, Department of philosophy, Gyeongsang National University, , Jinjoodaero 501, Inmoon 1 hogwan 404, 52828, Jinju-si, Gyeongsangnamdo, Republic of [...]

Features involving the taste, smell, touch, and sight of products, as well as attributes such as safety and confidence, are not easily measured in product research without respondents actually experiencing [...]

Business analytics has evolved from being a novelty used by a select few to an accepted facet of conducting business. Recommender systems form a critical component of the business analytics [...]

In this paper we use Bayes estimates of a multinomial probit model with fully flexible substitution patterns to forecast consumer response to ultra-low-emission vehicles. In this empirical application of the [...]

We investigate whether partners in a brand alliance should be similar or dissimilar in brand image to foster favorable perceptions of brand fit. Using a Bayesian nonlinear structural equation model [...]

Disaggregate demand in the marketplace exists on a grid determined by the package sizes offered by manufacturers and retailers. Although consumers may want to purchase a continuousvalued amount of a [...]

Social learning can occur when information is transferred from existing customers to potential customers. It is especially important when the information that is conveyed pertains to experience attributes, i.e., attributes [...]

Online advertising campaigns often consist of multiple ads, each with different creative content. We consider how various creatives in a campaign differentially affect behavior given the targeted individual s ad [...]

We reanalyze endogenous sample selection in the context of customer scoring, targeting, and influencing decisions. Scoring relies on ordered lists of probabilities that customers act in a way that contributes [...]

This paper considers the history of keywords used in Marketing Science to develop insights on the evolution of marketing science. Several findings emerge. First, 'pricing' and 'game theory' are the [...]

Most ads in practice receive no more than a single eye fixation. This study investigates the limits of what ads can communicate under such adverse exposure conditions. We find that [...]

Stock assessments that use only fisheries independent data have been developed from a number of different standpoints over the last 15 years. While the ability of such stock assessments to avoid the use of potentially compromised or hard to interpret commercial data or making certain assumptions that are common to traditional assessments has been established, little has been made of their potential for detecting and estimating complex mortality trends over time or their potential utility in survey-based management procedures. Using North Sea herring (Clupea harengus) data as an example, a Bayesian survey-based assessment method that is able to estimate all the key population variables is detailed. However, survival probability, and not fishing mortality that is conditional on natural mortality, is the key parameter. Reversible jump Markov chain Monte Carlo routines were developed to explore the range of ages over which survival separates into year and age effects (a common assumption in many stock assessments). Post hoc estimates of natural mortality suggest that changes over years and ages may have occurred in relativity to historic levels. The derivation of reference points based on survival probability and surplus biomass production are detailed as proxies for more common F-based reference points. The potential role for the outputs of such assessments in a management procedure sense is discussed. Au cours des 15 dernieres annees, on a mis au point des evaluations de stocks qui emploient seulement des donnees independantes de la peche et qui presentent un nombre de perspectives differentes. Leur capacite a eviter d'utiliser des donnees commerciales compromises ou difficiles a interpreter et a eviter de faire certaines presuppositions courantes dans les evaluations traditionnelles est bien etablie; on s'est cependant peu interesse a leur potentiel pour detecter et estimer des tendances complexes de mortalite en fonction du temps et a leur utilisation potentielle dans les procedures de gestion basees sur les inventaires. En utilisant des donnees sur les harengs (Clupea harengus) de la mer du Nord comme exemple, nous presentons les details d'une methode bayesienne d'evaluation basee sur les inventaires qui est capable d'estimer toutes les variables demographiques importantes, mais dans laquelle la probabilite de survie, non la mortalite due a la peche conditionnelle a la mortalite naturelle, est la variable principale. Nous avons mis au point des routines de Monte Carlo par chaine de Markov avec saut reversible pour explorer la gamme des ages durant lesquels on peut separer les effets de l'annee et de l'age sur la survie (une presupposition commune dans plusieurs evaluations de stocks); les estimations post-hoc de la mortalite naturelle indiquent que des changements ont pu se produire au cours des annees et des ages par rapport aux niveaux historiques. Nous presentons la derivation de points de reference bases sur la probabilite de survie et la production de biomasse excedentaire comme variables de remplacement des points de reference plus courants bases sur F et nous discutons du role potentiel des resultats de telles evaluations dans une perspective des procedures de gestion. [Traduit par la Redaction], Introduction The concept of integrated stock assessment (Hilborn and Walters 1992; Quinn and Deriso 1999; Hampton and Fournier 2001) is that the available data be brought together in some sort [...]

Catchability and natural mortality are key quantities in fisheries stock assessment. However, it is difficult to estimate these two parameters simultaneously using only fishery catch and effort data. A Bayesian state-space modified delay-difference model is outlined that can estimate time series of catchability by fleet as well as natural mortality. This model, and three variants thereof, is fitted to data for grooved tiger prawns (Penaeus semisulcatus) in Australia's Northern Prawn Fishery during the period of the year when there is little recruitment. A model that allows for both observation and process error and estimates natural mortality is best, in terms of model selection criteria as well as fit diagnostics. The posterior median estimate for catchability for the primary target fleet ranges from 6.15 X [10.sup.-4] to 1.09 X [10.sup.-4] during 1980-2007, while the posterior median estimate for catchability for a fleet with P. semisulcatus as its byproduct is about 20% of that for the primary fleet. Fishing efficiency increased at approximately 2% annually during 1980-2007, while the weekly natural mortality is estimated to be 0.053 [week.sup.-1]. La capturabilite et la mortalite naturelle sont deux quantites essentielles dans revaluation des stocks de peche. Il est, cependant, difficile d'estimer ces deux variables simultanement a partir de seulement les donnees de capture et d'effort de peche. Nous presentons un modele bayesien modifie d'etat-espace et de delai difference qui peut estimer des series chronologiques de capturabilite par la flotte de peche, ainsi que la mortalite naturelle. Nous ajustons ce modele et trois de ses variae la peche commerciale nordique de crevettes (Penaeus semisulcatus) de l'Australie durant la periode de l'annee ou il y a peu de recrutement. Un modele qui tient compte a la fois des erreurs d'observation et de traitement et qui estime la mortalite naturelle fonctionne le mieux, en ce qui a trait aux criteres de selection des modeles et aux caracteres diagnostics de l'ajustement. L'estimation a posteriori de la mediane de la capturabilite par la flotte qui cible principalement les crevettes varie de 6,15 X [10.sup.-4] a 1,09 X [10.sup.-4] entre 1980 et 2007, alors que l'estimation a posteriori de la mediane de la capturabilite par la flotte qui recolte P. semisulcatus comme capture accessoire est d'environ 20 % de celle de la flotte principale. L'efficacite de la peche a augmente d'environ 2 % par annee de 1980 a 2007, alors que la mortalite naturelle hebdomadaire est estimee a 0,053 [semaine.sup.-1]. [Traduit par la Redaction], Introduction Stock assessments provide the basis for applying harvest control rules and hence providing fisheries management advice. In general, stock assessment involves fitting population dynamics models to available data using [...]

The development of progress plans for each identified technical performance parameter (TPP) is a critical element of technical performance measurement. The measured values of TPPs are referred to as technical [...]

For high-dimensional data sets with complicated dependency structures, the full likelihood approach often leads to intractable computational complexity. This imposes difficulty on model selection, given that most traditionally used information criteria require evaluation of the full likelihood. We propose a composite likelihood version of the Bayes information criterion (BIC) and establish its consistency property for the selection of the true underlying marginal model. Our proposed BIC is shown to be selection-consistent under some mild regularity conditions, where the number of potential model parameters is allowed to increase to infinity at a certain rate of the sample size. Simulation studies demonstrate the empirical performance of this new BIC, especially for the scenario where the number of parameters increases with sample size. Technical proofs of our theoretical results are provided in the online supplemental materials. KEY WORDS: Consistency; Model selection; Pseudo-likelihood; Variable selection.

Copulas have proven to he very successful tools for the flexible modeling of cross-sectional dependence. In this paper we express the dependence structure of continuous-valued time series data using a sequence of bivariate copulas. This corresponds to a type of decomposition recently called a 'vine' in the graphical models literature, where each copula is entitled a 'pair-copula.' We propose a Bayesian approach for the estimation of this dependence structure for longitudinal data. Bayesian selection ideas are used to identify any independence pair-copulas, with the end result being a parsimonious representation of a time-inhomogeneous Markov process of varying order. Estimates are Bayesian model averages over the distribution of the lag structure of the Markov process. Using a simulation study we show that the selection approach is reliable and can improve the estimates of both conditional and unconditional pairwise dependencies substantially. We also show that a vine with selection outperforms a Gaussian copula with a flexible correlation matrix. The advantage of the pair-copula formulation is further demonstrated using a longitudinal model of intraday electricity load. Using Gaussian. Gumbel, and Clayton pair-copulas we identify parsimonious decompositions of intraday serial dependence, which improve the accuracy of intraday load forecasts. We also propose a new diagnostic for measuring the goodness of lit of high-dimensional multivariate copulas. Overall, the pair-copula model is very general and the Bayesian method generalizes many previous approaches for the analysis of longitudinal data. Supplemental materials for the article are also available online. KEY WORDS: Bayesian model selection; Copula diagnostic; Covarianee selection; D-vine; Goodness of fit; Inhomogeneous Markov process; Intraday electricity load; Longitudinal copulas.

To link to full-text access for this article, visit this link: http://dx.doi.org/10.1016/j.jedc.2010.05.008 Byline: Shirley J. Huang (a), Jun Yu (b) Abstract: In this paper a Markov chain Monte Carlo (MCMC) technique is developed for the Bayesian analysis of structural credit risk models with microstructure noises. The technique is based on the general Bayesian approach with posterior computations performed by Gibbs sampling. Simulations from the Markov chain, whose stationary distribution converges to the posterior distribution, enable exact finite sample inferences of model parameters. The exact inferences can easily be extended to latent state variables and any nonlinear transformation of state variables and parameters, facilitating practical credit risk applications. In addition, the comparison of alternative models can be based on deviance information criterion (DIC) which is straightforwardly obtained from the MCMC output. The method is implemented on the basic structural credit risk model with pure microstructure noises and some more general specifications using daily equity data from US and emerging markets. We find empirical evidence that microstructure noises are positively correlated with the firm values in emerging markets. Author Affiliation: (a) Lee Kong Chian School of Business, Singapore Management University, 50 Stamford Road, Singapore 178899, Singapore (b) School of Economics and Sim Kee Boon Institute for Financial Economics, Singapore Management University, 90 Stamford Road, Singapore 178903, Singapore

To understand the effects of environmental factors on a social system with multilevel society in proboscis monkey units, the temporal change of the local density of sleeping sites of monkeys was investigated along the Menanggul river from May 2005 to 2006 in Malaysia. Proboscis monkeys typically return to riverside trees for night sleeping. The sleeping site locations of a one-male unit (BE-unit) were recorded and the locations of other one-male and all-male units within 500 m of the BE-unit were verified. In addition, environmental factors (food availability, the water level of the river, and the river width) and copulation frequency of BE-unit were recorded. From the analyses of the distance from the BE-unit to the nearest neighbor unit, no spatial clumping of the sleeping sites of monkey units on a smaller scale was detected. The results of a Bayesian analysis suggest that the conditional local density around the BE-unit can be predicted by the spatial heterogeneity along the river and by the temporal change of food availability, that is, the local density of monkey units might increase due to better sleeping sites with regard to predator attacks and clumped food sources; proboscis monkeys might not exhibit high-level social organization previously reported. In addition, this study shows the importance of data analysis that considers the effects of temporal autocorrelation, because the daily measurements of longitudinal data on monkeys are not independent of each other. KEY WORDS Nasalis larvatus; multilevel; Bayesian statistical model DOI 10.1002/ajpa.21218

In this article we construct simultaneous confidence bands for a smooth curve using penalized spline estimators. We consider three types of estimation methods: (a) as a standard (fixed effect) nonparametric model, (b) using the mixed-model framework with the spline coefficients as random effects, and (c) a full Bayesian approach. The volume-of-tube formula is applied for the first two methods and compared with Bayesian simultaneous confidence bands from a frequentist perspective. We show that the mixed-model formulation of penalized splines can help obtain, at least approximately, confidence bands with either Bayesian or frequentist properties. Simulations and data analysis support the proposed methods. The R package ConfBands accompanies the article. KEY WORDS: B-spline; Bayesian penalized spline; Confidence band; Mixed model; Penalization.

We develop a class of models for inference about abundance or density using spatial capture-recapture data from studies based on camera trapping and related methods. The model is a hierarchical model composed of two components: a point process model describing the distribution of individuals in space (or their home range centers) and a model describing the observation of individuals in traps. We suppose that trap- and individual-specific capture probabilities are a function of distance between individual home range centers and trap locations. We show that the models can be regarded as generalized linear mixed models, where the individual home range centers are random effects. We adopt a Bayesian framework for inference under these models using a formulation based on data augmentation. We apply the models to camera trapping data on tigers from the Nagarahole Reserve, India, collected over 48 nights in 2006. For this study, 120 camera locations were used, but cameras were only operational at 30 locations during any given sample occasion. Movement of traps is common in many camera-trapping studies and represents an important feature of the observation model that we address explicitly in our application. Key words: abundance, Bayesian analysis; binomial point process; camera trapping; carnivore surveys; data augmentation; density estimation; hierarchical model; Markov chain Monte Carlo; spatial capturerecapture; tigers; trapping array; trapping grid.

This paper not only develops Bayesian analysis models for addressing both single-level and sequential multiple-level baggage screening problems but also provides a new perspective on the design and evaluation of aviation security systems. Using the Bayesian approach, the operator's prior knowledge about the status of a bag is incorporated with present data. The posterior distributions for single-level and multiple-level screenings are developed, respectively. To evaluate the performance for Bayesian screening, two metrics are introduced and implemented: (i) system risk bounded to the posterior mean of undetected threats; and (ii) system direct cost per bag that incorporates purchasing costs, operating costs and processing rates. By evaluating the trade-off between system risk and system cost, this paper assesses different screening technologies and combinations of them for single-level and two-level systems. The findings from numerical analyses provide recommendations for cost-effective selections of technologies with low risk levels. [Supplementary materials are available for this article. Go to the publisher's online edition of HE Transactions for the following free supplemental resource: Appendix] Keywords: Bayesian analysis, aviation baggage screening, single-level screening, multiple-level sequential screening, risk assessment, cost-effectiveness assessment, 1. Introduction The terrorist attacks on September 11, 2001, prompted significant policy and operational changes in aviation security systems. One of the important changes is that 100% of checked baggage [...]

Using data generated from laboratory experiments, we test and compare the empirical accuracy of two models that focus on judgment errors associated with processing information from random sequences. We test for regime-shifting beliefs of the type theorized in Barberis et al. (Barberis, N., A. Shleifer, R. Vishny. 1998. A model of investor sentiment. J. Financial Econom. 49(3) 307-343) and for beliefs in the 'law of small numbers' as modeled in Rabin (Rabin, M. 2002. Inference by believers in the law of small numbers. Quart. J. Econom. 117(3) 775-816). In our experiments, we show subjects randomly generated sequences of binary outcomes and ask them to provide probability assessments of the direction of the next outcome. Inconsistent with regime-shifting beliefs, we find that subjects are not more likely to predict that the current streak will continue the longer the streak. Instead, consistent with Rabin (2002), subjects are more likely to expect a reversal following short streaks and continuation after long streaks. Results of a 'test-of-fit' analysis based on structural estimation of each model also favor the model in Rabin. To provide more insight on Rabin, we use an additional experimental treatment to show that as the perception of the randomness of the outcome-generating process increases, subjects are more likely to predict reversals of current streaks. Key words: decision analysis; inference; experimental economics; behavioral finance History: Received October 5, 2006; accepted April 14, 2009, by George Wu, decision analysis. Published online in Articles in Advance August 27, 2009., 1. Introduction Experimental research by cognitive psychologists provides a multiplicity of evidence showing that cognitive biases have profound effects on belief formation and revision. Translating the wide variety of documented [...]

Age estimation of individuals is often an integral part of species management research, and a number of ageestimation techniques are commonly employed. Often, the error in these techniques is not quantified or accounted for in other analyses, particularly in growth curve models used to describe physiological responses to environment and human impacts. Also, noninvasive, quick, and inexpensive methods to estimate age are needed. This research aims to provide two Bayesian methods to (i) incorporate age uncertainty into an age--length Schnute growth model and (ii) produce a method from the growth model to estimate age from length. The methods are then employed for Florida manatee (Trichechus manatus) carcasses. After quantifying the uncertainty in the aging technique (counts of ear bone growth layers), we fit age--length data to the Schnute growth model separately by sex and season. Independent prior information about population age structure and the results of the Schnute model are then combined to estimate age from length. Results describing the age--length relationship agree with our understanding of manatee biology. The new methods allow us to estimate age, with quantified uncertainty, for 98% of collected carcasses: 36% from ear bones, 62% from length. L'estimation de l'age des individus fait souvent partie integrante de la recherche sur la gestion des especes et plusieurs methodes d'estimation de l'age sont en usage courant. Il arrive souvent que l'on ne mesure pas l'erreur associee a ces methodes ou qu'on n'en tienne pas compte dans les autres analyses, particulierement dans les modeles de courbe de croissance utilises pour decrire les reactions physiologiques aux impacts environnementaux et anthropiques. De plus, il existe un besoin de methodes non invasives, rapides et peu couteuses d'estimation de l'age. Notre recherche vise a fournir deux methodes bayesiennes pour (i) incorporer l'incertitude associee a l'age dans un modele de croissance age--longueur de Schnute et (ii) produire, a partir du modele de croissance, une methode pour estimer l'age a partir de la longueur. Nous avons ensuite utilise les methodes sur des carcasses de lamantins de Floride (Trichechus manatus). Apres avoir determine l'incertitude dans la technique de determination de l'age (denombrement des couches de croissance des osselets de l'oreille), nous avons ajuste les donnees d'age--longueur au modele de croissance de Schnute separement en fonction du sexe et de la saison. Nous avons ensuite combine des renseignements a priori independants au sujet de la structure en age de la population aux resultats du modele de Schnute pour estimer l'age a partir de la longueur. Les resultats qui decrivent la relation age--longueur s'accordent avec notre comprehension actuelle de la biologie des lamantins. Les nouvelles methodes nous ont permis d'estimer l'age, avec une incertitude connue, de 98 % des carcasses recoltees : 36 % a partir des osselets de l'oreille et 62 % a partir de la longueur. [Traduit par la Redaction], Introduction Age estimation of individuals is often an integral part of population dynamics research because species may have age-specific survival and reproductive rates that are vital components in determining population [...]

This paper extends a state-space Bayesian mark-recapture framework to multiple-recapture data to estimate fishery-specific capture and mortality rates and seasonal movement rates for fish in different length classes. The methodology is applied to tag recapture data for white sturgeon (Acipenser transmontanus) collected in the recreational fishery and the Canadian Department of Fisheries and Ocean's test fishery at Albion in the lower Fraser River. Significant differences were found between some estimated movement rates by season and length class, supporting the notion of there being marked differences in seasonal movement patterns between different life history stages of A. transmontanus in the lower Fraser River. Uncertainty in the tag reporting rate parameter, quantified using a recreational creel sampling program, is summarized by a prior distribution. The utility of recreational fishing effort as a model covariate in accounting for seasonal and spatial variation in recapture rates is addressed using Bayesian model evaluation criteria. The data provide strong support in favour of models that include fishing effort as a covariate. The appropriate level of stratification for the recreational catchability parameter q is assessed using Bayesian model evaluation criteria; models in which q is estimated by season and length class have the highest posterior probabilities. Notre travail elargit le cadre etat-espace bayesien de marquage-recapture aux donnees de recaptures multiples afin d'estimer les taux de capture et de mortalite specifiques a une peche donnee, ainsi que les taux de deplacements saisonniers des poissons des differentes classes de longueur. Nous appliquons la methodologie a des donnees de recuperation des etiquettes de l'esturgeon blanc (Acipenser transmontanus) obtenues lors de la peche sportive, de meme que lors de la peche experimentale du ministere canadien des Peches et Oceans, a Albion sur le cours inferieur du fleuve Fraser. Il existe des differences significatives entre certains taux estimes de deplacements en fonction de la saison et de la classe de longueur, ce qui appuie l'idee qu'il y a des differences importantes dans les patrons de deplacements saisonniers au cours des differents stades du cycle biologique d'A. transmontanus dans le Fraser inferieur. L'incertitude reliee au parametre du taux de retour des etiquettes, calcule d'apres un programme d'enquetes aupres des pecheurs, est resumee par une distribution a priori. L'utilite de l'effort de peche sportive comme covariable du modele pour tenir compte de la variation saisonniere et spatiale des taux de recapture est determinee a l'aide de criteres bayesiens d'evaluation des modeles. Les donnees appuient fortement les modeles qui incluent l'effort de peche comme l'une des covariables. Le niveau approprie de stratification du parametre de capturabilite a la peche sportive q est estime a l'aide de criteres bayesiens d'evaluation des modeles; les modeles dans lesquels q est estime en fonction de la saison et de la classe de longueur possedent les probabilites a posteriori les plus elevees. [Traduit par la Redaction], Introduction Variation in the spatial distribution and seasonal migration patterns of anadromous fish stocks resulting from life history stage specific patterns of habitat utilization may be an important feature of [...]

A methodology is proposed to efficiently model a random set via a multistage hierarchical Bayesian model. We define a NonOverlapping Random Disk Model (NORDM), which is similar in spirit to the well-known Poisson--Boolean model. This model is formulated in a conditional setting that facilitates Bayesian sampling of important parameters in the model. This framework can accommodate any object, not just those with disk shapes, although the model can be easily extended to include any known compact convex set instead of the disc (e.g., polygons or ellipses). We further propose a growth model that is conceptually simple and allows straightforward estimation of parameters, without the need for tedious calculations of hitting or inclusion probabilities. The model is applied to severe storm cell development as obtained from weather radar. KEY WORDS: Growth model; Hierarchical Bayesian estimation; Nonoverlapping random disc model; Poisson-Boolean model.

Bayesian model-based classifiers, both unsupervised and supervised, have been studied extensively, and their value and versatility have been demonstrated on a wide spectrum of applications within science and engineering. A majority of the classifiers are built on the assumption of intrinsic discreteness of the considered data features or on their discretization prior to the modeling. On the other hand, Gaussian mixture classifiers have also been utilized to a large extent for continuous features in the Bayesian framework. Often, the primary reason for discretization in the classification context is the simplification of the analytical and numerical properties of the Bayesian models. However, the discretization can be problematic due to its ad hoc nature and the decreased statistical power to detect the correct classes (or clusters) in the resulting procedure. Here, we introduce an unsupervised classification approach for fuzzy feature vectors that utilizes a discrete model structure while preserving the continuous characteristics of data. This goal is achieved by replacing the ordinary likelihood by a binomial quasi-likelihood to yield an analytical expression for the posterior probability of a given clustering solution. The resulting model can also be justified from an information-theoretic perspective. Our method is shown to yield highly accurate clusterings for challenging synthetic and empirical data sets and to perform favorably compared to some alternative approaches. Index Terms--Bayesian clustering, fuzzy modeling, quasi-likelihood, continuous data.

We present a statistical approach for identifying residues in DNA sequences for which diversity may be maintained by natural selection. Bayesian generalized linear models (GLMs) are used to describe patterns of mutation in a DNA sequence alignment. Posterior distributions of key quantities, such as probabilities of nonsynonymous and synonymous mutations per site, are studied. Inference in this class of models is achieved through customary Markov chain Monte Carlo methods. Model selection is dealt with by means of a minimum posterior predictive loss approach. We describe how information on the evolutionary process underlying the sequences can be formally incorporated into the models through structured priors. The proposed methodology was designed to analyze several DNA sequences encoding the vaccine candidate apical membrane antigen-1 (AMA-1) of the human malaria parasite Plasmodium falciparum. The study of genetic variability in antigen sequences is relevant to determining whether a particular antigen is a viable target for a vaccine construct. Using a simulation study, we first compare the GLM-based approach to existing methods for delecting sites under selection that are based on stochastic models of sequence evolution. We then apply the proposed models to the AMA-1 sequence data, which allows us to identify residues with the greatest disparities between nonsynonymous and synonymous changes. Recent experimental evidence suggests that several of these residues are immunologically relevant, indicating that the proposed models may be used predictively to identify functionally significant residues in antigens for which experimental results are not yet available. KEY WORDS: Bayesian generalized linear model; DNA sequence data: Malaria antigens; Model comparison; Mutation count data: Natural selection: Structured priors.

We investigate a solution to the problem of multisensor scene understanding by formulating it in the framework of Bayesian model selection and structure inference. Humans robustly associate multimodal data as appropriate, but previous modeling work has focused largely on optimal fusion, leaving segregation unaccounted for and unexploited by machine perception systems. We illustrate a unifying Bayesian solution to multisensory perception and tracking, which accounts for both integration and segregation by explicit probabilistic reasoning about data association in a temporal context. Such an explicit inference of multimodal data association is also of intrinsic interest for higher level understanding of multisensory data. We illustrate this by using a probabilistic implementation of data association in a multiparty audiovisual scenario, where unsupervised learning and structure inference is used to automatically segment, associate, and track individual subjects in audiovisual sequences. Indeed, the structure-inference-based framework introduced in this work provides the theoretical foundation needed to satisfactorily explain many confounding results in human psychophysics experiments involving multimodal cue integration and association. Index Terms--Sensor fusion, audiovisual, multimodal, detection, tracking, graphical models, model selection, Bayesian inference, speaker association.

We develop a Bayesian capture-recapture model that provides estimates of abundance as well as time-varying and heterogeneous survival and capture probability distributions. The model uses a state-space approach by incorporating an underlying population model and an observation model, and, here is applied to photo-identification data to estimate trends in the abundance and survival of a population of bottlenose dolphins (Tursiops truncatus) in northeast Scotland. Novel features of the model include simultaneous estimation of time-varying survival and capture probability distributions, estimation of heterogeneity effect for survival and capture, use of separate data to inflate the number of identified animals to the total abundance, and integration of separate observations of the same animals from right and left side photograph. A Bayesian approach using Markov chain Monte Carlo methods allows for uncertainty in measurement and parameters, and simulations confirm the model's validity. KEY WORDS: Abundance; Logits; Photo-identification; Survival; Trends.

Genomic alterations have been linked to the development and progression of cancer. The technique of comparative genomic hybridization (CGH) yields data consisting of ffluorescence intensity ratios of test and reference DNA samples. The intensity ratios provide information about the number of copies in DNA. Practical issues such as the contamination of tumor cells in tissue specimens and normalization errors necessitate the use of statistics for learning about the genomic alterations from array CGH data. As increasing amounts of array CGH data become available, there is a growing need for automated algorithms for characterizing genomic profiles. Specifically, there is a need for algorithms that can identify gains and losses in the number of copies based on statistical considerations, rather than merely detect trends in the data. We adopt a Bayesian approach, relying on the hidden Markov model to account for the inherent dependence in the intensity ratios. Posterior inferences are made about gains and losses in copy number. Localized amplifications (associated with oncogene mutations) and deletions (associated with mutations of tumor suppressors) are identified using posterior probabilities. Global trends such as extended regions of altered copy number are detected. Because the posterior distribution is analytically intractable, we implement a Metropolis-within-Gibbs algorithm for efficient simulation--based inference. Publicy available data on pancreatic adenocarcinoma, glioblastoma multiforme, and breast cancer are analyzed, and comparisons are made with some widely used algorithms to illustrate the reliability and success of the technique. KEY WORDS: Amplifications'. Cancer; Copy number; Deletions; DNA; Genomic aterations; Intensity ratios; MCMC; Tumor.

The Lasso estimate for linear regression parameters can be interpreted as a Bayesian posterior mode estimate when the regression parameters have independent Laplace (i.e., double-exponential) priors. Gibbs sampling from this posterior is possible using an expanded hierarchy with conjugate normal priors for the regression parameters and independent exponential priors on their variances. A connection with the inverse-Gaussian distribution provides tractable full conditional distributions. The Bayesian Lasso provides interval estimates (Bayesian credibleintervals) that can guide variable selection. Moreover, the structure of the hierarchical model provides both Bayesian and likelihood methods for selecting the Lasso parameter. Slight modifications lead to Bayesian versions of other Lasso-related estimation methods, including bridge regression and a robust variant. KEY WORDS: Empirical Bayes; Gibbs sampler: Hierarchical model; Inverse Gaussian; Linear regression; Penalized regression; Scale mixture of normals.

In this article we consider the relationship of covariates to the time to caries of permanent first molars. This involves an analysis of multivariate doubly interval-censored data. To describe this relationship. We suggest an accelerated failure time model with random effects, taking into account that the observations are clustered, Indeed, up to four permanent molars per child enter into the analysis, implying up to four caries times for each child. Each distributional part of the model is specified in a flexible way as a penalized Gaussian mixture with an overspecified number of mixture components. A Bayesian approach with the Markov chain Monte Carlo methodology is used to estimate the model parameters, and a software package in the R language has been written that implements it. KEY WORDS: Clustered data; Density smoothing, Gaussian Markov random field; Markov chain Carlo; Regression; Survival data.

This paper presents a Bayesian analysis of an ordered probit model with endogenous selection. The model can be applied when analyzing ordered outcomes that depend on endogenous covariates that are discrete choice indicators modeled by a multinomial probit model. The model is illustrated by analyzing the effects of different types of medical insurance plans on the level of hospital utilization, allowing for potential endogeneity of insurance status. The estimation is performed using the Markov chain Monte Carlo (MCMC) methods to approximate the posterior distribution of the parameters in the model. Keywords: Treatment effects; MCMC; Discontinuity regression

Zellner's g prior remains a popular conventional prior for use in Bayesian variable selection, despite several undesirable consistency issues. In this article we study mixtures of g priors as an alternative to default g priors that resolve many of the problems with the original formulation while maintaining the computational tractability that has made the g prior so popular. We present theoretical properties of the mixture g priors and provide real and simulated examples to compare the mixture formulation with fixed g priors, empirical Bayes approaches, and other default procedures. KEY WORDS: AIC; Bayesian model averaging; BIC; Cauchy; Empirical Bayes; Gaussian hypergeometric functions; Model selection; Zellner-Siow priors.

Beta distributions with both parameters equal to 0, 1/2, or 1 are the usual choices for 'noninformative' priors for Bayesian estimation of the binomial parameter. However, as illustrated by two examples from the Bayesian literature, care needs to be taken with parameter values below 1, both for noninformative and informative priors, as such priors concentrate their mass close to 0 and/or 1 and can suppress the importance of the observed data. These examples concern the case of no successes (or failures) and illustrate the informativeness of the Jeffreys prior usually recommended as the 'consensus prior.' In particular, the second example suggests that when the binomial parameter is known to be very small, an informative prior from the beta(1, b) family (b > 1) seems appropriate, while a beta(a, b) with a < 1 can be too informative. It is thus argued that sensitivity analysis of an informative prior should be based on a consensus posterior corresponding to the Bayes-Laplace prior rather than the Jeffreys prior. KEY WORDS: Bayesian inference; Binomial distribution; Noninformative priors; Prior families.

In this paper, we present a modulated Poisson process model to describe and analyze arrival data to a call center. The attractive feature of this model is that it takes into account both covariate and time effects on the call volume intensity, and in so doing, enables us to assess the effectiveness of different advertising strategies along with predicting the arrival patterns. A Bayesian analysis of the model is developed and an extension of the model is presented to describe potential heterogeneity in arrival patterns. The proposed model and the methodology are implemented using real call center arrival data. Key words: call center; advertising strategy; modulated Poisson process; Bayesian analysis; heterogeneity, 1. Introduction and Background Forecasting call volume data that is based on a specific advertisement or promotion plan poses a challenging problem for call centers. The need for optimal scheduling [...]

We propose Bayesian parametric and semiparametric partially linear regression methods to analyze the outcome-dependent follow-up data when the random time of a follow-up measurement of an individual depends on the history of both observed longitudinal outcomes and previous measurement times. We begin with the investigation of the simplifying assumptions of Lipsitz, Fitzmaurice, Ibrahim, Gelber, and Lipshultz, and present a new model for analyzing such data by allowing subject-specific correlations for the longitudinal response and by introducing a subject-specific latent variable to accommodate the association between the longitudinal measurements and the follow-up times. An extensive simulation study shows that our Bayesian partially linear regression method facilitates accurate estimation of the true regression line and the regression parameters. We illustrate our new methodology using data from a longitudinal observational study. KEY WORDS: Bayesian cubic smoothing spline; Latent variable; Partially linear model.

The direct simulation method, which has enabled exact Bayesian inference for discrete mixture distributions, is used for continuous mixture distribution models. This method is compared with the particle filters and Gibbs sampler by using simulated data and real datasets.

A general formulation is provided for Bayesian model-based clustering, where the subset labels are exchangeable up to covariate effects. The statistical performance of the optimal clustering is compared with methods like hierarchical clustering, which are model-based but ad hoc in their detailed definition.

This paper presents a newly developed simulation-based approach for Bayesian model updating, model class selection, and model averaging called the transitional Markov chain Monte Carlo (TMCMC) approach. The idea behind TMCMC is to avoid the problem of sampling from difficult target probability density functions (PDFs) but sampling from a series of intermediate PDFs that converge to the target PDF and are easier to sample. The TMCMC approach is motivated by the adaptive Metropolis--Hastings method developed by Beck and Au in 2002 and is based on Markov chain Monte Carlo. It is shown that TMCMC is able to draw samples from some difficult PDFs (e.g., multimodal PDFs, very peaked PDFs, and PDFs with flat manifold). The TMCMC approach can also estimate evidence of the chosen probabilistic model class conditioning on the measured data, a key component for Bayesian model class selection and model averaging. Three examples are used to demonstrate the effectiveness of the TMCMC approach in Bayesian model updating, model class selection, and model averaging. DOI: 10.1061/(ASCE)0733-9399(2007)133:7(816) CE Database subject headings: Bayesian analysis; Simulation; Markov chains; Monte Carlo method.

This paper extends the conventional Bayesian mixture of normals model by permitting state probabilities to depend on observed covariates. The dependence is captured by a simple multinomial probit model. A conventional and rapidly mixing MCMC algorithm provides access to the posterior distribution at modest computational cost. This model is competitive with existing econometric models, as documented in the paper's illustrations. The first illustration studies quantiles of the distribution of earnings of men conditional on age and education, and shows that smoothly mixing regressions are an attractive alternative to nonBayesian quantile regression. The second illustration models serial dependence in the S&P 500 return, and shows that the model compares favorably with ARCH models using out of sample likelihood criteria. JEL classification: C11; C14; C15

We propose a natural conjugate prior for the instrumental variables regression model. The prior is a natural conjugate one since the marginal prior and posterior of the structural parameter have the same functional expressions which directly reveal the update from prior to posterior. The Jeffreys prior results from a specific setting of the prior parameters and results in a marginal posterior of the structural parameter that has an identical functional form as the sampling density of the limited information maximum likelihood estimator. We construct informative priors for the Angrist--Krueger [1991. Does compulsory school attendance affect schooling and earnings? Quarterly Journal of Economics 106, 979-1014] data and show that the marginal posterior of the return on education in the US coincides with the marginal posterior from the Southern region when we use the Jeffreys prior. This result occurs since the instruments are the strongest in the Southern region and the posterior using the Jeffreys prior, identical to maximum likelihood, focusses on the strongest available instruments. We construct informative priors for the other regions that make their posteriors of the return on education similar to that of the US and the Southern region. These priors show the amount of prior information needed to obtain comparable results for all regions. JEL classification: C11 Keywords: Instrumental variables; Rank reduction; Natural conjugate prior; Bayesian analysis

Scanner data for fast moving consumer goods typically amount to panels of time series where both N and T are large. To reduce the number of parameters and to shrink parameters towards plausible and interpretable values, Hierarchical Bayes models turn out to be useful. Such models contain in the second level a stochastic model to describe the parameters in the first level. In this paper we propose such a model for weekly scanner data where we explicitly address (i) weekly seasonality when not many years of data are available and (ii) non-linear price effects due to historic reference prices. We discuss representation and inference and we propose a Markov Chain Monte Carlo sampler to obtain posterior results. An illustration to a market-response model for 96 brands for about 8 years of weekly data shows the merits of our approach. JEL classification: C11; C22; C23; M31 Keywords: Panels of time series; Weekly seasonality; Threshold models; Non-linearity; Hierarchical Bayes; MCMC

Empirical modeling of Bayes equilibrium for decentralized trading mechanism of Web-based second price auctions is presented.