Sequential Monte Carlo Samplers
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Machine scores (provisional)
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- Teacher spread
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- Validation status
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Abstract
Summary We propose a methodology to sample sequentially from a sequence of probability distributions that are defined on a common space, each distribution being known up to a normalizing constant. These probability distributions are approximated by a cloud of weighted random samples which are propagated over time by using sequential Monte Carlo methods. This methodology allows us to derive simple algorithms to make parallel Markov chain Monte Carlo algorithms interact to perform global optimization and sequential Bayesian estimation and to compute ratios of normalizing constants. We illustrate these algorithms for various integration tasks arising in the context of Bayesian inference.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
The record
- Venue
- Journal of the Royal Statistical Society Series B (Statistical Methodology)
- Topic
- Bayesian Methods and Mixture Models
- Field
- Computer Science
- Canadian institutions
- University of British Columbia
- Funders
- Engineering and Physical Sciences Research Council
- Keywords
- Markov chain Monte CarloMonte Carlo methodHybrid Monte CarloComputer scienceMonte Carlo integrationAlgorithmBayesian inferenceBayesian probabilityQuasi-Monte Carlo methodProbability distributionMathematical optimizationMathematicsArtificial intelligenceStatistics
- Has abstract in OpenAlex
- yes