【摘 要】
：
A new algorithm is motivated by the similarities and differences between the Metropolis-Hastings (MH) algorithm and sampling importance/ resampling (SIR). B
【机 构】
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SichuanUniversity,China
【出 处】
：
The 24th International Workshop on Matrices and Statistics(第
论文部分内容阅读
A new algorithm is motivated by the similarities and differences between the Metropolis-Hastings (MH) algorithm and sampling importance/ resampling (SIR). Both algorithms sample from a pool of candidates that is very small for MH but is huge for SIR. The MH candidates are local and change in every iteration, while the pool of SIR is global but fixed, that is, never updated. We propose using a pool of candidates that is not huge but large enough—with the aid of a quasi-random sequence—to search the entire support, and refreshing the pool constantly. The new sampler begins with a quasi-random sequence as the candidates pool, then iterates among the following three steps: (ⅰ) computes the likelihood of all the candidates; (ⅱ) selects a sample from the candidates according to the likelihood; and (ⅲ) creates a new pool of candidates by independently randomizing the current pool. Thus, it generates independent samples like SIR but without the difficulties of designing a problem-specific proposal distribution and of producing a huge pool.
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