On population-based simulation for static inference

Authors:
Ajay Jasra;David A. Stephens;Christopher C. Holmes
Affiliations:
Department of Mathematics, Imperial College London, London, UK SW7 2AZ;Department of Mathematics and Statistics, McGill University, Montreal, Canada H3A 2K6;Department of Statistics, University of Oxford, Oxford, UK OX1 3TG
Venue:
Statistics and Computing
Year:
2007

Citing 7
Cited 22

On sequential Monte Carlo sampling methods for Bayesian filtering

Statistics and Computing
Annealed importance sampling

Statistics and Computing
On the swapping algorithm

Random Structures & Algorithms
Adaptive Selection Methods for Genetic Algorithms

Proceedings of the 1st International Conference on Genetic Algorithms
Monte Carlo Statistical Methods (Springer Texts in Statistics)

Monte Carlo Statistical Methods (Springer Texts in Statistics)
On learning strategies for evolutionary Monte Carlo

Statistics and Computing
Monte Carlo Strategies in Scientific Computing

Monte Carlo Strategies in Scientific Computing

Annealing stochastic approximation Monte Carlo algorithm for neural network training

Machine Learning
Interacting sequential Monte Carlo samplers for trans-dimensional simulation

Computational Statistics & Data Analysis
Computational methods for complex stochastic systems: a review of some alternatives to MCMC

Statistics and Computing
Stereo Matching Using Population-Based MCMC

International Journal of Computer Vision
Estimating Bayes factors via thermodynamic integration and population MCMC

Computational Statistics & Data Analysis
Parallel and interacting Markov chain Monte Carlo algorithm

Mathematics and Computers in Simulation
Importance tempering

Statistics and Computing
A path sampling identity for computing the Kullback-Leibler and J divergences

Computational Statistics & Data Analysis
2010 Special Issue: Bayesian estimation of phase response curves

Neural Networks
Bayesian identification of a cracked plate using a population-based Markov Chain Monte Carlo method

Computers and Structures
Annealing evolutionary stochastic approximation Monte Carlo for global optimization

Statistics and Computing
Tuning tempered transitions

Statistics and Computing
Smooth functional tempering for nonlinear differential equation models

Statistics and Computing
Parallel hierarchical sampling: A general-purpose interacting Markov chains Monte Carlo algorithm

Computational Statistics & Data Analysis
Free energy computations by minimization of Kullback-Leibler divergence: An efficient adaptive biasing potential method for sparse representations

Journal of Computational Physics
Parallel tempering MCMC acceleration using reconfigurable hardware

ARC'12 Proceedings of the 8th international conference on Reconfigurable Computing: architectures, tools and applications
Abnormal object detection by canonical scene-based contextual model

ECCV'12 Proceedings of the 12th European conference on Computer Vision - Volume Part III
Interacting multiple try algorithms with different proposal distributions

Statistics and Computing
On adaptive Metropolis---Hastings methods

Statistics and Computing
Window annealing for pixel-labeling problems

Computer Vision and Image Understanding
Adaptive Markov chain Monte Carlo for auxiliary variable method and its application to parallel tempering

Neural Networks
Marginal reversible jump Markov chain Monte Carlo with application to motor unit number estimation

Computational Statistics & Data Analysis

Quantified Score

Hi-index	0.00

Visualization

Abstract

In this paper we present a review of population-based simulation for static inference problems. Such methods can be described as generating a collection of random variables {X n } n=1,驴,N in parallel in order to simulate from some target density 驴 (or potentially sequence of target densities). Population-based simulation is important as many challenging sampling problems in applied statistics cannot be dealt with successfully by conventional Markov chain Monte Carlo (MCMC) methods. We summarize population-based MCMC (Geyer, Computing Science and Statistics: The 23rd Symposium on the Interface, pp. 156---163, 1991; Liang and Wong, J. Am. Stat. Assoc. 96, 653---666, 2001) and sequential Monte Carlo samplers (SMC) (Del Moral, Doucet and Jasra, J. Roy. Stat. Soc. Ser. B 68, 411---436, 2006a), providing a comparison of the approaches. We give numerical examples from Bayesian mixture modelling (Richardson and Green, J. Roy. Stat. Soc. Ser. B 59, 731---792, 1997).