A sequential particle filter method for static models

By Nicolas Chopin. 2002

699 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Global and Planetary Change

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Citations in the corpus

Kernel Sequential Monte Carlo (2015, methodology): “In this paper, we focus on static SMC methods, which recently have generated increasing interest as an alternative to MCMC for Bayesian inference on a single target distribution [3, 4, 6, 11].”
The Variational Gaussian Process (2015, background): “Although we haven’t found it necessary in practice, see Appendix E for scaling the size of variational data.”
Gradient Importance Sampling (2015, background): “This approach is very robust when compared to MCMC algorithms, as it explores the probability space better [5, 3].”
On sequential Monte Carlo, partial rejection control and approximate Bayesian computation (2008, background): “diate distributions 1; 2;:::; n 1. We consider the data tempering approach ( Chopin 2002;”
Provable Bayesian Inference via Particle Mirror Descent (2015, methodology): “The static SMC uses simple normal distribution (Chopin, 2002) or kernel density estimation (Balakrishnan and Madigan, 2006) for rejuvenation.”
Efficient Thompson Sampling for Online Matrix-Factorization Recommendation (2015, methodology): “In this paper, we instead employ a sequential Monte-Carlo (SMC) method for online Bayesian inference [7, 8].”
A Rational Analysis of Rule-Based Concept Learning (2008, methodology): “(We have, in fact, verified some of the simulation results presented in this paper using a similar on-line sequential Monte Carlo algorithm, based upon Chopin (2002), but have not evaluated this algorithm for psychological relevance.”
A Review of Modern Computational Algorithms for Bayesian Optimal Design (2016, methodology): “(2014) have recently conducted work on using SMC algorithms (Chopin, 2002) to search for optimal designs for mixed effects models in the presence of parameter and model uncertainty.”
Sequential Monte Carlo on large binary sampling spaces (2011, background): “The most convenient and somewhat natural strategy is the geometric bridge (Gelman and Meng, 1998; Neal, 2001; Del Moral et al., 2006) Alternatively, one could use a sequences of mixtures or, in a Bayesian context, a sequences of posterior distributions where data is added as see (Chopin, 2002).”
Using Random Quasi-Monte-Carlo Within Particle Filters, With Application to Financial Time Series (2005, background): “As a result there has been some recent interest in applying particle (cid:12)lter methods to batch problems (Chopin, 2002; Fearnhead, 2004), particularly for problems where the data sets can be very large (Ridgeway and Madigan, 2003).”
Long-term stability of sequential Monte Carlo methods under verifiable conditions (2012, methodology): “The original proof of the CLT was later simplified and extended to more general particle filtering algorithms in [3, 14, 16, 21, 24]; in Section 2 we recall in detail the version obtained in [14] and provide an explicit expression of the asymptotic”
Sequential importance sampling for structural reliability analysis (2016, methodology): “SIS was developed in the statistical community for exploring posterior distributions and estimating normalizing constants in the context of Bayesian analysis [11, 12, 13].”
Monte Carlo Confidence Sets for Identified Sets (2016, background): “21See Chopin (2002, 2004) and Del Moral, Doucet, and Jasra (2006) for the generic SMC algorithm for estimating static model parameters.”
Particle Markov chain Monte Carlo methods (2010, background): “This includes models which naturally have a sequential structure (e.g. Liu (2001)), but also models which do not have such a structure and for which the user induces such a structure (e.g. Chopin (2002) and Del Moral et al. (2006)).”
Modeling the effects of memory on human online sentence processing with particle filters (2008, methodology): “As a consequence, applications of the particle filter in machine learn ing and statistics tend to supplement the basic algorithm with additional steps such as running Markov chain Monte Carlo on the particles in order to re-introduce diversity (e.g., [ 28 ]).”