Book contents
- Frontmatter
- Contents
- List of contributors
- Preface
- 1 Inference and estimation in probabilistic time series models
- I Monte Carlo
- 2 Adaptive Markov chain Monte Carlo: theory and methods
- 3 Auxiliary particle filtering: recent developments
- 4 Monte Carlo probabilistic inference for diffusion processes: a methodological framework
- II Deterministic approximations
- III Switching models
- IV Multi-object models
- V Nonparametric models
- VI Agent-based models
- Index
- Plate section
- References
3 - Auxiliary particle filtering: recent developments
from I - Monte Carlo
Published online by Cambridge University Press: 07 September 2011
Book contents
- Frontmatter
- Contents
- List of contributors
- Preface
- 1 Inference and estimation in probabilistic time series models
- I Monte Carlo
- 2 Adaptive Markov chain Monte Carlo: theory and methods
- 3 Auxiliary particle filtering: recent developments
- 4 Monte Carlo probabilistic inference for diffusion processes: a methodological framework
- II Deterministic approximations
- III Switching models
- IV Multi-object models
- V Nonparametric models
- VI Agent-based models
- Index
- Plate section
- References
Summary
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- Information
- Bayesian Time Series Models , pp. 52 - 81Publisher: Cambridge University PressPrint publication year: 2011
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