Nested Sampling with Constrained Hamiltonian Monte Carlo
Nested Sampling with Constrained Hamiltonian Monte Carlo
复制标题
使用约束哈密顿蒙特卡罗进行嵌套采样
DOI：
10.1063/1.3573613
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发表时间：
2010
期刊：
影响因子：
--
通讯作者：
M. Betancourt
中科院分区：
文献类型：
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作者：
M. Betancourt
嵌套抽样是贝叶斯推理的一种强有力的方法，最终受到从严格约束的概率分布中进行抽样的计算要求的限制。一个有效的算法在其本身的权利，汉密尔顿蒙特卡罗是很容易适应有效地从任何光滑，约束分布的样本。利用这种受约束的汉密尔顿蒙特卡罗，我介绍了嵌套抽样算法的一般实现。
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.