Parallelized nested sampling
Parallelized nested sampling
复制标题
并行嵌套采样
DOI：
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发表时间：
2014
期刊：
影响因子：
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通讯作者：
P. Goggans
中科院分区：
文献类型：
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作者：
R. W. Henderson;P. Goggans
嵌套抽样作为MCMC技术的一个重要优点是它能够从多模态分布和其他退化分布中提取代表性样本。这种覆盖是通过在可能性约束范围内保持一些所谓的活样本来实现的。在通常的实践中，在每一步中，只有可能性最小的样本从这组活样本中被丢弃并替换。在b[1]中，Skilling表明，对于给定数量的活样本，仅丢弃一个样本在对数证据的估计中产生最高的精度。但是，如果我们增加活样本的数量，则可以在保持相同精度的情况下同时丢弃更多的样本。对于仅串行运行的计算机代码，这种修改将大大增加达到收敛所需的挂钟时间。但是，如果我们使用具有并行处理能力的计算机，并且我们编写代码来利用这种并行性来替换多个示例……
One of the important advantages of nested sampling as an MCMC technique is its ability to draw representative samples from multimodal distributions and distributions with other degeneracies. This coverage is accomplished by maintaining a number of so-called live samples within a likelihood constraint. In usual practice, at each step, only the sample with the least likelihood is discarded from this set of live samples and replaced. In [1], Skilling shows that for a given number of live samples, discarding only one sample yields the highest precision in estimation of the log-evidence. However, if we increase the number of live samples, more samples can be discarded at once while still maintaining the same precision. For computer code running only serially, this modification would considerably increase the wall clock time necessary to reach convergence. However, if we use a computer with parallel processing capabilities, and we write our code to take advantage of this parallelism to replace multiple samples ...