Bayesian Analysis for the Social Sciences

Jackman, Simon

doi:10.1002/9780470686621

Cited by 625 publications

(456 citation statements)

References 57 publications

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“…In all cases, conditional posterior distributions of parameters may be derived straightforwardly from Bayes rule (Gelman, Carlin, Stern, & Rubin, 2004;Jackman, 2009;Rouder & Lu, 2005).…”

Section: Parameter Estimationmentioning

confidence: 99%

Developing constraint in bayesian mixed models.

Haaf¹,

Rouder²

2017

Psychological Methods

117

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Model comparison in Bayesian mixed models is becoming popular in psychological science. Here we develop a set of nested models that account for order restrictions across individuals in psychological tasks. An order-restricted model addresses the question "Does everybody," as in "Does everybody show the usual Stroop effect," or "Does everybody respond more quickly to intense noises than subtle ones?" The crux of the modeling is the instantiation of 10s or 100s of order restrictions simultaneously, one for each participant. To our knowledge, the problem is intractable in frequentist contexts but relatively straightforward in Bayesian ones. We develop a Bayes factor model-comparison strategy using Zellner and Siow's default g-priors appropriate for assessing whether effects obey equality and order restrictions. We apply the methodology to seven data sets from Stroop, Simon, and Eriksen interference tasks. Not too surprisingly, we find that everybody Stroops-that is, for all people congruent colors are truly named more quickly than incongruent ones. But, perhaps surprisingly, we find these order constraints are violated for some people in the Simon task, that is, for these people spatially incongruent responses occur truly more quickly than congruent ones! Implications of the modeling and conjectures about the task-related differences are discussed.

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“…In all cases, conditional posterior distributions of parameters may be derived straightforwardly from Bayes rule (Gelman, Carlin, Stern, & Rubin, 2004;Jackman, 2009;Rouder & Lu, 2005).…”

Section: Parameter Estimationmentioning

confidence: 99%

Developing constraint in bayesian mixed models.

Haaf¹,

Rouder²

2017

Psychological Methods

117

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“…Perhaps as a result, the Bayesian treatment of the logit model has not seen widespread adoption by non-statisticians in the way that, for example, the Bayesian probit model is used extensively in both political science and market research (e.g. Rossi et al, 2005;Jackman, 2009). The lack of a standard computational approach also makes it more difficult to use the logit link in the kind of complex hierarchical models that have become routine in Bayesian statistics.…”

Section: Introductionmentioning

confidence: 99%

Bayesian Inference for Logistic Models Using Pólya–Gamma Latent Variables

Polson

Scott

Windle

2013

Journal of the American Statistical Association

771

1,070

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We propose a new data-augmentation strategy for fully Bayesian inference in models with binomial likelihoods. The approach appeals to a new class of Pólya-Gamma distributions, which are constructed in detail. A variety of examples are presented to show the versatility of the method, including logistic regression, negative binomial regression, nonlinear mixed-effects models, and spatial models for count data. In each case, our data-augmentation strategy leads to simple, effective methods for posterior inference that: (1) circumvent the need for analytic approximations, numerical integration, or Metropolis-Hastings; and (2) outperform other known data-augmentation strategies, both in ease of use and in computational efficiency. All methods, including an efficient sampler for the Pólya-Gamma distribution, are implemented in the R package BayesLogit.In the technical supplement appended to the end of the paper, we provide further details regarding the generation of Pólya-Gamma random variables; the empirical benchmarks reported in the main manuscript; and the extension of the basic dataaugmentation framework to contingency tables and multinomial outcomes.

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“…During the last three decades, Bayesian methods have developed greatly and are now widely established in many research areas, from clinical trials (Berry et al, 2011), to health economic assessment (Baio, 2012) to the social sciences (Jackman, 2009), to epidemiology (Greenland, 2006).…”

Section: Introductionmentioning

confidence: 99%

Spatial and spatio-temporal models with R-INLA

Blangiardo

Cameletti

Baio

et al. 2013

Spatial and Spatio-temporal Epidemiology

421

426

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During the last three decades, Bayesian methods have developed greatly in the field of epidemiology. Their main challenge focusses around computation, but the advent of Markov Chain Monte Carlo methods (MCMC) and in particular of the WinBUGS software has opened the doors of Bayesian modelling to the wide research community. However model complexity and database dimension still remain a constraint.Recently the use of Gaussian random fields has become increasingly popular in epidemiology as very often epidemiological data are characterised by a spatial and/or temporal structure which needs to be taken into account in the inferential process. The Integrated Nested Laplace Approximation (INLA) approach has been developed as a computationally efficient alternative to MCMC and the availability of an R package (R-INLA) allows researchers to easily apply this method.In this paper we review the INLA approach and present some applications on spatial and spatio-temporal data.

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Bayesian Analysis for the Social Sciences

Cited by 625 publications

References 57 publications

Developing constraint in bayesian mixed models.

Developing constraint in bayesian mixed models.

Bayesian Inference for Logistic Models Using Pólya–Gamma Latent Variables

Spatial and spatio-temporal models with R-INLA

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