Bayesian Auxiliary Variable Models for Binary and Multinomial Regression
Bayesian Auxiliary Variable Models for Binary and Multinomial Regression
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DOI：
10.1214/06-ba105
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发表时间：
2006-01-01
影响因子：
4.4
通讯作者：
Held, Leonhard
中科院分区：
文献类型：
--
作者：
Holmes, Chris C.;Held, Leonhard
在本文中，我们讨论贝叶斯二元和多项回归的辅助变量方法。这些方法非常适合自动化马尔可夫链蒙特卡罗模拟。在第一部分中，我们描述了一种使用联合更新的简单技术，该技术可以提高传统概率回归算法的性能。在第二部分中，我们讨论贝叶斯逻辑回归中推理的辅助变量方法，包括协变量集不确定性。最后，我们展示了如何将逻辑方法轻松扩展到多项回归模型。所有算法都是全自动的，无需用户设置参数和不必要的 Metropolis-Hastings 接受/拒绝步骤。
In this paper we discuss auxiliary variable approaches to Bayesian binary and multinomial regression. These approaches are ideally suited to aut omated Markov chain Monte Carlo simulation. In the first part we describe a simple technique using joint updating that improves the performance of the conventional probit regression algorithm. In the second part we discuss auxiliary variable methods for inference in Bayesian logistic regression, including covariate set uncertainty. Finally, we show how the logistic method is easily extended to multinomial regression models. All of the algorithms are fully automatic with no user set parameters and nonecessary Metropolis-Hastings accept/reject steps.