Sequential Monte Carlo Samplers¶
Del Moral, P., Doucet, A., & Jasra, A. (2006). Sequential Monte Carlo Samplers. Journal of the Royal Statistical Society: Series B, 411-436.
Cited by¶
1 citation across 1 artifact.
Each citation links to the sentence it supports in the citing article.
Domain-specific¶
- Particle Filter
- - Not every sequential Monte Carlo algorithm. Modern SMC samplers can traverse an artificial sequence of static target distributions, perform normalizing-constant estimation, or support optimization and batch Bayesian computation; their index need not be physical time or a hidden-state filtering index.
This sourceExtends SMC to general sequences of probability distributions, establishing why bare SMC is broader than hidden-state particle filtering.
- - Not every sequential Monte Carlo algorithm. Modern SMC samplers can traverse an artificial sequence of static target distributions, perform normalizing-constant estimation, or support optimization and batch Bayesian computation; their index need not be physical time or a hidden-state filtering index.
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Registry ID ref:c951ef312b03 · see in the full table