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Adrian F. M. Smith
Researcher at Imperial College London
Publications - 73
Citations - 36886
Adrian F. M. Smith is an academic researcher from Imperial College London. The author has contributed to research in topics: Bayesian probability & Bayesian statistics. The author has an hindex of 39, co-authored 73 publications receiving 35622 citations. Previous affiliations of Adrian F. M. Smith include University of Nottingham.
Papers
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Journal ArticleDOI
Novel approach to nonlinear/non-Gaussian Bayesian state estimation
TL;DR: An algorithm, the bootstrap filter, is proposed for implementing recursive Bayesian filters, represented as a set of random samples, which are updated and propagated by the algorithm.
BookDOI
Sequential Monte Carlo methods in practice
TL;DR: This book presents the first comprehensive treatment of Monte Carlo techniques, including convergence results and applications to tracking, guidance, automated target recognition, aircraft navigation, robot navigation, econometrics, financial modeling, neural networks, optimal control, optimal filtering, communications, reinforcement learning, signal enhancement, model averaging and selection.
Journal ArticleDOI
Sampling-Based Approaches to Calculating Marginal Densities
TL;DR: In this paper, three sampling-based approaches, namely stochastic substitution, the Gibbs sampler, and the sampling-importance-resampling algorithm, are compared and contrasted in relation to various joint probability structures frequently encountered in applications.
Journal Article
Sampling-based approaches to calculating marginal densities
TL;DR: Stochastic substitution, the Gibbs sampler, and the sampling-importance-resampling algorithm can be viewed as three alternative sampling- (or Monte Carlo-) based approaches to the calculation of numerical estimates of marginal probability distributions.
Journal ArticleDOI
Bayesian Computation Via the Gibbs Sampler and Related Markov Chain Monte Carlo Methods
TL;DR: The use of the Gibbs sampler for Bayesian computation is reviewed and illustrated in the context of some canonical examples as discussed by the authors, and comments are made on the advantages of sample-based approaches for inference summaries.