Nested Sampling with Constrained Hamiltonian Monte Carlo

被引：0

作者：

Betancourt, Michael ^{[1
]}

机构：

[1] MIT, Cambridge, MA 02139 USA

来源：

BAYESIAN INFERENCE AND MAXIMUM ENTROPY METHODS IN SCIENCE AND ENGINEERING | 2010年 / 1305卷

关键词：

Bayesian Inference; Nested Sampling; Hamiltonian Monte Carlo;

D O I：

暂无

中图分类号：

O414.1 [热力学];

学科分类号：

摘要：

Nested sampling is a powerful approach to Bayesian inference ultimately limited by the computationally demanding task of sampling from a heavily constrained probability distribution. An effective algorithm in its own right, Hamiltonian Monte Carlo is readily adapted to efficiently sample from any smooth, constrained distribution. Utilizing this constrained Hamiltonian Monte Carlo, I introduce a general implementation of the nested sampling algorithm.

引用

页码：165 / 172

页数：8

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