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Markov Chain Importance Sampling - a highly efficient estimator for MCMC (2020)

Klebanov, Ilja (PhD) ; Schuster, Ingmar (PhD)

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Publication Date: 2021-07-06

Description: Markov chain (MC) algorithms are ubiquitous in machine learning and statistics and many other disciplines. Typically, these algorithms can be formulated as acceptance rejection methods. In this work we present a novel estimator applicable to these methods, dubbed Markov chain importance sampling (MCIS), which efficiently makes use of rejected proposals. For the unadjusted Langevin algorithm, it provides a novel way of correcting the discretization error. Our estimator satisfies a central limit theorem and improves on error per CPU cycle, often to a large extent. As a by-product it enables estimating the normalizing constant, an important quantity in Bayesian machine learning and statistics.

Language: English

Type: article , doc-type:article

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