Elements of information theory
Elements of information theory
On sequential Monte Carlo sampling methods for Bayesian filtering
Statistics and Computing
Inference in Hidden Markov Models (Springer Series in Statistics)
Inference in Hidden Markov Models (Springer Series in Statistics)
Computational methods for complex stochastic systems: a review of some alternatives to MCMC
Statistics and Computing
Adaptive importance sampling in general mixture classes
Statistics and Computing
Improving the Performance of the Two-Stage Sampling Particle Filter: A Statistical Perspective
SSP '07 Proceedings of the 2007 IEEE/SP 14th Workshop on Statistical Signal Processing
IJCAI'05 Proceedings of the 19th international joint conference on Artificial intelligence
Enhanced importance sampling: unscented auxiliary particle filtering for visual tracking
AI'04 Proceedings of the 17th Australian joint conference on Advances in Artificial Intelligence
Efficient particle filtering for jump Markov systems. Application to time-varying autoregressions
IEEE Transactions on Signal Processing
A Basic Convergence Result for Particle Filtering
IEEE Transactions on Signal Processing
Editorial: Special issue on adaptive Monte Carlo methods
Statistics and Computing
Improved cross-entropy method for estimation
Statistics and Computing
Expert Systems with Applications: An International Journal
Optimal SIR algorithm vs. fully adapted auxiliary particle filter: a non asymptotic analysis
Statistics and Computing
| Hi-index | 0.00 |
In this paper we discuss new adaptive proposal strategies for sequential Monte Carlo algorithms--also known as particle filters--relying on criteria evaluating the quality of the proposed particles. The choice of the proposal distribution is a major concern and can dramatically influence the quality of the estimates. Thus, we show how the long-used coefficient of variation (suggested by Kong et al. in J. Am. Stat. Assoc. 89(278---288):590---599, 1994) of the weights can be used for estimating the chi-square distance between the target and instrumental distributions of the auxiliary particle filter. As a by-product of this analysis we obtain an auxiliary adjustment multiplier weight type for which this chi-square distance is minimal. Moreover, we establish an empirical estimate of linear complexity of the Kullback-Leibler divergence between the involved distributions. Guided by these results, we discuss adaptive designing of the particle filter proposal distribution and illustrate the methods on a numerical example.