Feb. 12, 2024, 5:42 a.m. | Xinzhu Liang Sanjaya Lohani Joseph M. Lukens Brian T. Kirby Thomas A. Searles Kody J. H. Law

cs.LG updates on arXiv.org arxiv.org

In the general framework of Bayesian inference, the target distribution can only be evaluated up-to a constant of proportionality. Classical consistent Bayesian methods such as sequential Monte Carlo (SMC) and Markov chain Monte Carlo (MCMC) have unbounded time complexity requirements. We develop a fully parallel sequential Monte Carlo (pSMC) method which provably delivers parallel strong scaling, i.e. the time complexity (and per-node memory) remains bounded if the number of asynchronous processes is allowed to grow. More precisely, the pSMC has …

bayesian bayesian inference complexity consistent cs.lg distribution framework general inference markov mcmc requirements scaling stat.co stat.ml

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