Particle Filtering for Partially Observed Gaussian State Space Models
- 1 October 2002
- journal article
- Published by Oxford University Press (OUP) in Journal of the Royal Statistical Society Series B: Statistical Methodology
- Vol. 64 (4), 827-836
- https://doi.org/10.1111/1467-9868.00363
Abstract
Summary: Solving Bayesian estimation problems where the posterior distribution evolves over time through the accumulation of data has many applications for dynamic models. A large number of algorithms based on particle filtering methods, also known as sequential Monte Carlo algorithms, have recently been proposed to solve these problems. We propose a special particle filtering method which uses random mixtures of normal distributions to represent the posterior distributions of partially observed Gaussian state space models. This algorithm is based on a marginalization idea for improving efficiency and can lead to substantial gains over standard algorithms. It differs from previous algorithms which were only applicable to conditionally linear Gaussian state space models. Computer simulations are carried out to evaluate the performance of the proposed algorithm for dynamic tobit and probit models.Keywords
This publication has 13 references indexed in Scilit:
- Following a Moving Target—Monte Carlo Inference for Dynamic Bayesian ModelsJournal of the Royal Statistical Society Series B: Statistical Methodology, 2001
- Sequential Monte Carlo Methods in PracticePublished by Springer Science and Business Media LLC ,2001
- Particle Filters — A Theoretical PerspectivePublished by Springer Science and Business Media LLC ,2001
- Combined Parameter and State Estimation in Simulation-Based FilteringPublished by Springer Science and Business Media LLC ,2001
- Mixture Kalman FiltersJournal of the Royal Statistical Society Series B: Statistical Methodology, 2000
- On sequential Monte Carlo sampling methods for Bayesian filteringStatistics and Computing, 2000
- Conditional Prior Proposals in Dynamic ModelsScandinavian Journal of Statistics, 1999
- Simulation‐based likelihood inference for limited dependent processesThe Econometrics Journal, 1998
- Bayesian Analysis of Binary and Polychotomous Response DataJournal of the American Statistical Association, 1993
- Sampling-Based Approaches to Calculating Marginal DensitiesJournal of the American Statistical Association, 1990