Model evidence from nonequilibrium simulations.

Habeck, M.

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Model evidence from nonequilibrium simulations.

Habeck, M. (2017). Model evidence from nonequilibrium simulations.

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Item Permalink: https://hdl.handle.net/21.11116/0000-0002-B7AC-E Version Permalink: https://hdl.handle.net/21.11116/0000-0002-B7AE-C

Genre: Conference Paper

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Creators:
Habeck, M.¹, Author

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1Research Group of Statistical Inverse-Problems in Biophysics, MPI for Biophysical Chemistry, Max Planck Society, ou_1113580

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Abstract: The marginal likelihood, or model evidence, is a key quantity in Bayesian parameter estimation and model comparison. For many probabilistic models, computation of the marginal likelihood is challenging, because it involves a sum or integral over an enormous parameter space. Markov chain Monte Carlo (MCMC) is a powerful approach to compute marginal likelihoods. Various MCMC algorithms and evidence estimators have been proposed in the literature. Here we discuss the use of nonequilibrium techniques for estimating the marginal likelihood. Nonequilibrium estimators build on recent developments in statistical physics and are known as annealed importance sampling (AIS) and reverse AIS in probabilistic machine learning. We introduce estimators for the model evidence that combine forward and backward simulations and show for various challenging models that the evidence estimators outperform forward and reverse AIS.

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Language(s): eng - English

Dates: Date issued: 2017

Publication Status: Issued

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Title: 31st Conference on Neural Information Processing Systems (NIPS)

Place of Event: Long Beach, CA

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Title: Advances in Neural Information Processing Systems

Source Genre: Series

Creator(s):
Guyon, I., Editor
Luxburg, U. V., Editor
Bengio, S., Editor
Wallach, H., Editor
Fergus, R., Editor
Vishwanathan, S., Editor
Garnett, R., Editor

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-

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Pages: 10 Volume / Issue: 30 Sequence Number: - Start / End Page: - Identifier: -