# Sequential Monte Carlo samplers

@article{Moral2002SequentialMC, title={Sequential Monte Carlo samplers}, author={Pierre Del Moral and A. Doucet}, journal={Journal of the Royal Statistical Society: Series B (Statistical Methodology)}, year={2002}, volume={68} }

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…

## 1,531 Citations

### Sequential Monte Carlo with transformations

- Computer ScienceStat. Comput.
- 2020

This paper uses sequential Monte Carlo samplers for performing Bayesian inference sequentially on a sequence of posteriors on spaces of different dimensions to yield an extremely flexible and general algorithm for Bayesian model comparison that is suitable for use in applications where the acceptance rate in reversible jump Markov chain Monte Carlo is low.

### Sequentially interacting Markov chain Monte Carlo methods

- Mathematics, Computer Science
- 2010

The proposed Sequentially Interacting Markov Chain Monte Carlo scheme works by generating interacting non-Markovian sequences which behave asymptotically like independent Metropolis-Hastings Markov chains with the desired limiting distributions.

### An Invitation to Sequential Monte Carlo Samplers

- Mathematics, Computer Science
- 2020

This article describes sequential Monte Carlo samplers and their possible implementations, arguing that they remain under-used in statistics, despite their ability to perform sequential inference and to leverage parallel processing resources among other potential benefits.

### Sequential Monte Carlo for Graphical Models

- Mathematics, Computer ScienceNIPS
- 2014

A new framework for how to use sequential Monte Carlo (SMC) algorithms for inference in probabilistic graphical models (PGM) is proposed and one of the key merits of the SMC sampler is that it provides an unbiased estimate of the partition function of the model.

### Sequential Markov Chain Monte Carlo

- Mathematics, Computer Science
- 2013

A sequential Markov chain Monte Carlo (SMCMC) algorithm to sample from a sequence of probability distributions, corresponding to posterior distributions at different times in on-line applications, which has advantages over sequential Monte Carlo in avoiding particle degeneracy issues.

### Convergence of Monte Carlo distribution estimates from rival samplers

- Computer ScienceStat. Comput.
- 2016

This article addresses how to optimally divide sampling effort between the samplers of the different distributions, and proposes a new Monte Carlo divergence error criterion based on Jensen–Shannon divergence.

### A sequential Monte Carlo approach to computing tail probabilities in stochastic models

- Mathematics, Computer Science
- 2011

It is shown how resampling weights can be chosen to yield logarithmically ecient Monte Carlo estimates of large deviation probabilities for multidimensional Markov random walks.

### Monte Carlo convergence of rival samplers

- Computer Science
- 2013

This article addresses how to optimally divide sampling effort between the samplers of the different distributions, and proposes a new Monte Carlo divergence error criterion based on Jensen-Shannon divergence.

### Finite Sample Complexity of Sequential Monte Carlo Estimators

- Mathematics, Computer Science
- 2018

Borders for the complexity of sequential Monte Carlo approximations for a variety of target distributions including finite spaces, product measures, and log-concave distributions including Bayesian logistic regression are obtained.

### On sequential Monte Carlo, partial rejection control and approximate Bayesian computation

- Mathematics, Computer ScienceStat. Comput.
- 2012

It is proved that the new sampler can reduce the variance of the incremental importance weights when compared with standard sequential Monte Carlo samplers, and provide a central limit theorem.

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