Stochastic simulation
Hidden Markov models for speech recognition
Technometrics
Bayesian forecasting and dynamic models (2nd ed.)
Bayesian forecasting and dynamic models (2nd ed.)
Probabilistic Networks and Expert Systems
Probabilistic Networks and Expert Systems
On sequential Monte Carlo sampling methods for Bayesian filtering
Statistics and Computing
A Sequential Monte Carlo Method for Bayesian Analysis of Massive Datasets
Data Mining and Knowledge Discovery
Particle filters for mixture models with an unknown number of components
Statistics and Computing
Exact and efficient Bayesian inference for multiple changepoint problems
Statistics and Computing
Model fitting and inference under latent equilibrium processes
Statistics and Computing
On population-based simulation for static inference
Statistics and Computing
A Randomized Quasi-Monte Carlo Simulation Method for Markov Chains
Operations Research
Iterated importance sampling in missing data problems
Computational Statistics & Data Analysis
Particle filters for state-space models with the presence ofunknown static parameters
IEEE Transactions on Signal Processing
Exact Bayesian curve fitting and signal segmentation
IEEE Transactions on Signal Processing
Factor graphs and the sum-product algorithm
IEEE Transactions on Information Theory
Editorial: Special issue on adaptive Monte Carlo methods
Statistics and Computing
Adaptive methods for sequential importance sampling with application to state space models
Statistics and Computing
A Statistical Reduced-Reference Approach to Digital Image Quality Assessment
ICCVG 2008 Proceedings of the International Conference on Computer Vision and Graphics: Revised Papers
A novel image template matching based on particle filtering optimization
Pattern Recognition Letters
A fast image analysis technique for the line tracking robots
ICAISC'10 Proceedings of the 10th international conference on Artifical intelligence and soft computing: Part II
Efficient Bayesian analysis of multiple changepoint models with dependence across segments
Statistics and Computing
A regularized bridge sampler for sparsely sampled diffusions
Statistics and Computing
Inference for non-linear diffusions and jump-diffusions: a Monte Carlo EM approach
ACMIN'12 Proceedings of the 14th international conference on Automatic Control, Modelling & Simulation, and Proceedings of the 11th international conference on Microelectronics, Nanoelectronics, Optoelectronics
Optimal SIR algorithm vs. fully adapted auxiliary particle filter: a non asymptotic analysis
Statistics and Computing
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We consider analysis of complex stochastic models based upon partial information. MCMC and reversible jump MCMC are often the methods of choice for such problems, but in some situations they can be difficult to implement; and suffer from problems such as poor mixing, and the difficulty of diagnosing convergence. Here we review three alternatives to MCMC methods: importance sampling, the forward-backward algorithm, and sequential Monte Carlo (SMC). We discuss how to design good proposal densities for importance sampling, show some of the range of models for which the forward-backward algorithm can be applied, and show how resampling ideas from SMC can be used to improve the efficiency of the other two methods. We demonstrate these methods on a range of examples, including estimating the transition density of a diffusion and of a discrete-state continuous-time Markov chain; inferring structure in population genetics; and segmenting genetic divergence data.