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A tutorial on bridgesampling Quentin F. GronauAlexandra SarafoglouDóra Matzke2017 год

A tutorial on bridge sampling
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Аннотация: The marginal likelihood plays an important role in many areas of Bayesian statistics such as parameter estimation, model comparison, and model averaging. In most applications, however, the marginal likelihood is not analytically tractable and must be approximated using numerical methods. Here we provide a tutorial on bridge sampling (Bennett, 1976; Meng & Wong, 1996), a reliable and relatively straightforward sampling method that allows researchers to obtain the marginal likelihood for models of varying complexity. First, we introduce bridge sampling and three related sampling methods using the beta-binomial model as a running example. We then apply bridge sampling to estimate the marginal likelihood for the Expectancy Valence (EV) model—a popular model for reinforcement learning. Our results indicate that bridge sampling provides accurate estimates for both a single participant and a hierarchical version of the EV model. We conclude that bridge sampling is an attractive method for mathematical psychologists who typically aim to approximate the marginal likelihood for a limited set of possibly high-dimensional models.
Год издания: 2017
Авторы: Quentin F. Gronau, Alexandra Sarafoglou, Dóra Matzke, Alexander Ly, Udo Boehm, Maarten Marsman, David S. Leslie, Jonathan J. Forster, Eric‐Jan Wagenmakers, Helen Steingroever
Издательство: Elsevier BV
Источник: Journal of Mathematical Psychology
Ключевые слова: Statistical Methods and Bayesian Inference, Statistical Methods in Clinical Trials, Statistical Methods and Inference
Другие ссылки: Journal of Mathematical Psychology (HTML)
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