An introduction to Sequential Monte Carlo for Bayesian inference and model comparison-with examples for psychology and behavioral science.

IF 4.6 2区 心理学 Q1 PSYCHOLOGY, EXPERIMENTAL
Max Hinne
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引用次数: 0

Abstract

Bayesian inference is becoming an increasingly popular framework for statistics in the behavioral sciences. However, its application is hampered by its computational intractability - almost all Bayesian analyses require a form of approximation. While some of these approximate inference algorithms, such as Markov chain Monte Carlo (MCMC), have become well known throughout the literature, other approaches exist that are not as widespread. Here, we provide an introduction to another family of approximate inference techniques known as Sequential Monte Carlo (SMC). We show that SMC brings a number of benefits, which we illustrate in three different examples: linear regression and variable selection for depression, growth curve mixture modeling of grade point averages, and in computational modeling of the Iowa Gambling Task. These use cases demonstrate that SMC is efficient in exploring posterior distributions, reaching similar predictive performance as state-of-the-art MCMC approaches in less wall-clock time. Moreover, they show that SMC is effective in dealing with multi-modal distributions, and that SMC not only approximates the posterior distribution but simultaneously provides a useful estimate of the marginal likelihood, which is the essential quantity in Bayesian model comparison. All of this comes at no additional effort from the end user.

介绍贝叶斯推理和模型比较的顺序蒙特卡罗-与心理学和行为科学的例子。
贝叶斯推理正在成为行为科学中越来越受欢迎的统计框架。然而,它的应用受到其计算难解性的阻碍——几乎所有贝叶斯分析都需要一种近似形式。虽然这些近似推理算法中的一些,如马尔可夫链蒙特卡罗(MCMC),已经在整个文献中变得众所周知,但其他方法并不普遍。在这里,我们介绍了另一种近似推理技术,即顺序蒙特卡罗(SMC)。我们表明SMC带来了许多好处,我们通过三个不同的例子来说明:抑郁症的线性回归和变量选择,平均成绩的增长曲线混合建模,以及爱荷华赌博任务的计算建模。这些用例表明,SMC在探索后验分布方面是有效的,在更短的时间内达到了与最先进的MCMC方法相似的预测性能。此外,他们还表明SMC在处理多模态分布方面是有效的,并且SMC不仅近似后验分布,而且同时提供了有用的边际似然估计,这是贝叶斯模型比较中必不可少的量。所有这些都不需要终端用户额外的努力。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
CiteScore
10.30
自引率
9.30%
发文量
266
期刊介绍: Behavior Research Methods publishes articles concerned with the methods, techniques, and instrumentation of research in experimental psychology. The journal focuses particularly on the use of computer technology in psychological research. An annual special issue is devoted to this field.
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