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On sequential Monte Carlo sampling methods for Bayesian filtering
An overview of methods for sequential simulation from posterior distributions for discrete time dynamic models that are typically nonlinear and non-Gaussian, and how to incorporate local linearisation methods similar to those which have previously been employed in the deterministic filtering literature are shown.
Particle Markov chain Monte Carlo methods
It is shown here how it is possible to build efficient high dimensional proposal distributions by using sequential Monte Carlo methods, which allows not only to improve over standard Markov chain Monte Carlo schemes but also to make Bayesian inference feasible for a large class of statistical models where this was not previously so.
Sequential Monte Carlo samplers
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 is proposed.
A Tutorial on Particle Filtering and Smoothing: Fifteen years later
A complete, up-to-date survey of particle filtering methods as of 2008, including basic and advanced particle methods for filtering as well as smoothing.
An Introduction to MCMC for Machine Learning
This purpose of this introductory paper is to introduce the Monte Carlo method with emphasis on probabilistic machine learning and review the main building blocks of modern Markov chain Monte Carlo simulation.
Sequential Monte Carlo Methods in Practice
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.
The Unscented Particle Filter
This paper proposes a new particle filter based on sequential importance sampling that outperforms standard particle filtering and other nonlinear filtering methods very substantially and is in agreement with the theoretical convergence proof for the algorithm.
Rao-Blackwellised Particle Filtering for Dynamic Bayesian Networks
It is shown that Rao-Blackwellised particle filters (RBPFs) lead to more accurate estimates than standard PFs, and are demonstrated on two problems, namely non-stationary online regression with radial basis function networks and robot localization and map building.
An Introduction to Sequential Monte Carlo Methods
- A. Doucet, N. D. Freitas, N. Gordon
- Computer Science, MathematicsSequential Monte Carlo Methods in Practice
Many real-world data analysis tasks involve estimating unknown quantities from some given observations, and all inference on the unknown quantities is based on the posterior distribution obtained from Bayes’ theorem.
Sequential Monte Carlo methods for multitarget filtering with random finite sets
Random finite sets (RFSs) are natural representations of multitarget states and observations that allow multisensor multitarget filtering to fit in the unifying random set framework for data fusion.…