• 主题
高级搜索  >
历史搜索 清空历史
首页> 外文期刊>Statistics and computing >Computing marginal likelihoods via the Fourier integral theorem and pointwise estimation of posterior densities
【24h】

Computing marginal likelihoods via the Fourier integral theorem and pointwise estimation of posterior densities

机译：Computing marginal likelihoods via the Fourier integral theorem and pointwise estimation of posterior densities

获取原文
获取原文并翻译 | 示例
           
页面导航
  • 摘要
  • 著录项
  • 相关主题

摘要

Abstract In this paper, we present a novel approach to the estimation of a density function at a specific chosen point. With this approach, we can estimate a normalizing constant, or equivalently compute a marginal likelihood, by focusing on estimating a posterior density function at a point. Relying on the Fourier integral theorem, the proposed method is capable of producing quick and accurate estimates of the marginal likelihood, regardless of how samples are obtained from the posterior; that is, it uses the posterior output generated by a Markov chain Monte Carlo sampler to estimate the marginal likelihood directly, with no modification to the form of the estimator on the basis of the type of sampler used. Thus, even for models with complicated specifications, such as those involving challenging hierarchical structures, or for Markov chains obtained from a black-box MCMC algorithm, the method provides a straightforward means of quickly and accurately estimating the marginal likelihood. In addition to developing theory to support the favorable behavior of the estimator, we also present a number of illustrative examples.

著录项

获取原文

客服邮箱：kefu@zhangqiaokeyan.com

京公网安备：11010802029741号 ICP备案号：京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有

  • 客服微信

  • 服务号