Waste-Free Sequential Monte Carlo

نویسندگان

چکیده

Abstract A standard way to move particles in a sequential Monte Carlo (SMC) sampler is apply several steps of Markov chain (MCMC) kernel. Unfortunately, it not clear how many need be performed for optimal performance. In addition, the output intermediate are discarded and thus wasted somehow. We propose new, waste-free SMC algorithm which uses outputs all these MCMC as particles. establish that its consistent asymptotically normal. use expression asymptotic variance develop various insights on implement practice. particular method estimate, from single run algorithm, any particle estimate. show empirically, through range numerical examples, tends outperform samplers, especially so situations where mixing considered kernels decreases across iterations (as tempering or rare event problems).

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ژورنال

عنوان ژورنال: Journal of The Royal Statistical Society Series B-statistical Methodology

سال: 2021

ISSN: ['1467-9868', '1369-7412']

DOI: https://doi.org/10.1111/rssb.12475