Particle Filters and Markov Chains for Learning of Dynamical Systems.
Saved in:
| Superior document: | Linköping Studies in Science and Technology. Dissertations Series ; v.1530 |
|---|---|
| : | |
| Place / Publishing House: | Linköping : : Linkopings Universitet,, 2013. {copy}2013. |
| Year of Publication: | 2013 |
| Edition: | 1st ed. |
| Language: | English |
| Series: | Linköping Studies in Science and Technology. Dissertations Series
|
| Online Access: | |
| Physical Description: | 1 online resource (65 pages) |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Table of Contents:
- Intro
- Abstract
- Populärvetenskaplig sammanfattning
- Acknowledgments
- Contents
- Notation
- I Background
- 1 Introduction
- 1.1 Models of dynamical systems
- 1.2 Inference and learning
- 1.3 Contributions
- 1.4 Publications
- 1.5 Thesis outline
- 1.5.1 Outline of Part I
- 1.5.2 Outline of Part II
- 2 Learning of dynamical systems
- 2.1 Modeling
- 2.2 Maximum likelihood
- 2.3 Bayesian learning
- 2.4 Data augmentation
- 2.5 Online learning
- 3 Monte Carlo methods
- 3.1 The Monte Carlo idea
- 3.2 Rejection Sampling
- 3.3 Importance sampling
- 3.4 Particle filters and Markov chains
- 3.5 Rao-Blackwellization
- 4 Concluding remarks
- 4.1 Conclusions and future work
- 4.2 Further reading
- Bibliography.