Bayesian Estimation of Individual-behavior Models PDF Download
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Author: Andrés Ignacio Musalem Said
Publisher:
ISBN:
Category :
Languages : en
Pages : 154
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Book Description
Author: Andrés Ignacio Musalem Said
Publisher:
ISBN:
Category :
Languages : en
Pages : 154
Get Book Here
Book Description
Author: Andrés Ignacio Musalem
Publisher:
ISBN:
Category :
Languages : en
Pages : 154
Get Book Here
Book Description
Author: Jean-Paul Fox
Publisher: Springer Science & Business Media
ISBN: 1441907424
Category : Social Science
Languages : en
Pages : 323
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Book Description
The modeling of item response data is governed by item response theory, also referred to as modern test theory. The eld of inquiry of item response theory has become very large and shows the enormous progress that has been made. The mainstream literature is focused on frequentist statistical methods for - timating model parameters and evaluating model t. However, the Bayesian methodology has shown great potential, particularly for making further - provements in the statistical modeling process. The Bayesian approach has two important features that make it attractive for modeling item response data. First, it enables the possibility of incorpor- ing nondata information beyond the observed responses into the analysis. The Bayesian methodology is also very clear about how additional information can be used. Second, the Bayesian approach comes with powerful simulation-based estimation methods. These methods make it possible to handle all kinds of priors and data-generating models. One of my motives for writing this book is to give an introduction to the Bayesian methodology for modeling and analyzing item response data. A Bayesian counterpart is presented to the many popular item response theory books (e.g., Baker and Kim 2004; De Boeck and Wilson, 2004; Hambleton and Swaminathan, 1985; van der Linden and Hambleton, 1997) that are mainly or completely focused on frequentist methods. The usefulness of the Bayesian methodology is illustrated by discussing and applying a range of Bayesian item response models.
Author: Jerome R. Busemeyer
Publisher:
ISBN: 0199957991
Category : Psychology
Languages : en
Pages : 425
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Book Description
This Oxford Handbook offers a comprehensive and authoritative review of important developments in computational and mathematical psychology. With chapters written by leading scientists across a variety of subdisciplines, it examines the field's influence on related research areas such as cognitive psychology, developmental psychology, clinical psychology, and neuroscience. The Handbook emphasizes examples and applications of the latest research, and will appeal to readers possessing various levels of modeling experience. The Oxford Handbook of Computational and mathematical Psychology covers the key developments in elementary cognitive mechanisms (signal detection, information processing, reinforcement learning), basic cognitive skills (perceptual judgment, categorization, episodic memory), higher-level cognition (Bayesian cognition, decision making, semantic memory, shape perception), modeling tools (Bayesian estimation and other new model comparison methods), and emerging new directions in computation and mathematical psychology (neurocognitive modeling, applications to clinical psychology, quantum cognition). The Handbook would make an ideal graduate-level textbook for courses in computational and mathematical psychology. Readers ranging from advanced undergraduates to experienced faculty members and researchers in virtually any area of psychology--including cognitive science and related social and behavioral sciences such as consumer behavior and communication--will find the text useful.
Author: Roy Levy
Publisher: CRC Press
ISBN: 1439884684
Category : Mathematics
Languages : en
Pages : 480
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Book Description
A Single Cohesive Framework of Tools and Procedures for Psychometrics and Assessment Bayesian Psychometric Modeling presents a unified Bayesian approach across traditionally separate families of psychometric models. It shows that Bayesian techniques, as alternatives to conventional approaches, offer distinct and profound advantages in achieving many goals of psychometrics. Adopting a Bayesian approach can aid in unifying seemingly disparate—and sometimes conflicting—ideas and activities in psychometrics. This book explains both how to perform psychometrics using Bayesian methods and why many of the activities in psychometrics align with Bayesian thinking. The first part of the book introduces foundational principles and statistical models, including conceptual issues, normal distribution models, Markov chain Monte Carlo estimation, and regression. Focusing more directly on psychometrics, the second part covers popular psychometric models, including classical test theory, factor analysis, item response theory, latent class analysis, and Bayesian networks. Throughout the book, procedures are illustrated using examples primarily from educational assessments. A supplementary website provides the datasets, WinBUGS code, R code, and Netica files used in the examples.
Author: Rens van de Schoot
Publisher: Routledge
ISBN: 1000760944
Category : Psychology
Languages : en
Pages : 270
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Book Description
Researchers often have difficulties collecting enough data to test their hypotheses, either because target groups are small or hard to access, or because data collection entails prohibitive costs. Such obstacles may result in data sets that are too small for the complexity of the statistical model needed to answer the research question. This unique book provides guidelines and tools for implementing solutions to issues that arise in small sample research. Each chapter illustrates statistical methods that allow researchers to apply the optimal statistical model for their research question when the sample is too small. This essential book will enable social and behavioral science researchers to test their hypotheses even when the statistical model required for answering their research question is too complex for the sample sizes they can collect. The statistical models in the book range from the estimation of a population mean to models with latent variables and nested observations, and solutions include both classical and Bayesian methods. All proposed solutions are described in steps researchers can implement with their own data and are accompanied with annotated syntax in R. The methods described in this book will be useful for researchers across the social and behavioral sciences, ranging from medical sciences and epidemiology to psychology, marketing, and economics.
Author: Peter D. Congdon
Publisher: CRC Press
ISBN: 1498785913
Category : Mathematics
Languages : en
Pages : 580
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Book Description
An intermediate-level treatment of Bayesian hierarchical models and their applications, this book demonstrates the advantages of a Bayesian approach to data sets involving inferences for collections of related units or variables, and in methods where parameters can be treated as random collections. Through illustrative data analysis and attention to statistical computing, this book facilitates practical implementation of Bayesian hierarchical methods. The new edition is a revision of the book Applied Bayesian Hierarchical Methods. It maintains a focus on applied modelling and data analysis, but now using entirely R-based Bayesian computing options. It has been updated with a new chapter on regression for causal effects, and one on computing options and strategies. This latter chapter is particularly important, due to recent advances in Bayesian computing and estimation, including the development of rjags and rstan. It also features updates throughout with new examples. The examples exploit and illustrate the broader advantages of the R computing environment, while allowing readers to explore alternative likelihood assumptions, regression structures, and assumptions on prior densities. Features: Provides a comprehensive and accessible overview of applied Bayesian hierarchical modelling Includes many real data examples to illustrate different modelling topics R code (based on rjags, jagsUI, R2OpenBUGS, and rstan) is integrated into the book, emphasizing implementation Software options and coding principles are introduced in new chapter on computing Programs and data sets available on the book’s website
Author: Scott M. Lynch
Publisher: Springer Science & Business Media
ISBN: 0387712658
Category : Social Science
Languages : en
Pages : 376
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Book Description
This book outlines Bayesian statistical analysis in great detail, from the development of a model through the process of making statistical inference. The key feature of this book is that it covers models that are most commonly used in social science research - including the linear regression model, generalized linear models, hierarchical models, and multivariate regression models - and it thoroughly develops each real-data example in painstaking detail.
Author: Torben Hansen
Publisher: Springer Science & Business Media
ISBN: 1402079559
Category : Business & Economics
Languages : en
Pages : 132
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Book Description
Retailing and consumer patronage behavior constitute fascinating research areas within the field of marketing. Retailing contributes to an increasing proportion of gross national products and employment but is, however, also faced with problems and opportunities like increased product complexity, rapidly changing consumer expectations, and the introduction of new technologies. Also, consumers are facing markets of increasingly complexity when making decisions on how to conduct their behavior, primarily as a result of new technologies, shorter products life cycles in general, and higher complexity of products and services. In this book, we present and deal with various topics in relation to retailing and consumer patronage behavior. Together, these topics involve different problem settings and draw on different theories, models and statistical techniques. However, it is common to all the results presented in the following chapters (with the exception of chapter II) that they, in total or in part, rest on a major survey, which was conducted by the authors in 1999. Our now retired colleague, Hans Engstrøm participated in preparing this survey and did a great job in providing research ideas. For this, and for many stimulating discussions, we are highly grateful.
Author: Jerome R. Busemeyer
Publisher: Elsevier Inc. Chapters
ISBN: 012807311X
Category : Medical
Languages : en
Pages : 33
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Book Description
This chapter provides a brief overview of all the steps of computational modeling and illustrates their use in cognitive and decision neuroscience. The chapter starts with a simple example model developed for a popular “decision from experience” type of task. Second, the chapter discusses the important issue concerning analysis of group versus individual data. Third, methods for estimating model parameters are presented, which includes least squares, maximum likelihood, Bayesian estimation, and hierarchical Bayesian estimation. Fourth methods for model comparison are discussed such as R-square, chi-square, Akaike information criterion, Bayesian information criterion, generalization criterion, and cross validation. Finally the importance of using these methods are illustrated with an example model based fMRI application.
