近似贝叶斯计算
计算机科学
边际似然
贝叶斯定理
推论
贝叶斯推理
贝叶斯概率
统计推断
状态空间
贝叶斯因子
统计模型
机器学习
算法
人工智能
计算
数学
统计
作者
Minh‐Ngoc Tran,David J. Nott,Robert Kohn
标识
DOI:10.1080/10618600.2017.1330205
摘要
Variational Bayes (VB) is rapidly becoming a popular tool for Bayesian inference in statistical modeling. However, the existing VB algorithms are restricted to cases where the likelihood is tractable, which precludes their use in many interesting situations such as in state--space models and in approximate Bayesian computation (ABC), where application of VB methods was previously impossible. This article extends the scope of application of VB to cases where the likelihood is intractable, but can be estimated unbiasedly. The proposed VB method therefore makes it possible to carry out Bayesian inference in many statistical applications, including state--space models and ABC. The method is generic in the sense that it can be applied to almost all statistical models without requiring too much model-based derivation, which is a drawback of many existing VB algorithms. We also show how the proposed method can be used to obtain highly accurate VB approximations of marginal posterior distributions. Supplementary material for this article is available online.
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