Name
Abstract | ||
|---|---|---|
The Expectation-Maximization (EM) algorithm in combination with particle filters is a powerful tool that can solve very complex problems, such as parameter estimation in general nonlinear non-Gaussian state space models. We here apply the recently proposed online EM algorithm to parameter estimation in jump Markov models, that contain both continuous and discrete states. In particular, we focus on estimating process and measurement noise distributions being modeled as mixtures of members from the exponential family. |
Year | Venue | Keywords |
|---|---|---|
2012 | Information Fusion | Gaussian processes,Markov processes,expectation-maximisation algorithm,parameter estimation,particle filtering (numerical methods),expectation-maximization algorithm,jump Markov systems,nonlinear non-Gaussian state space models,online EM algorithm,parameter estimation,particle filters |
Field | DocType | ISBN |
Markov process,Forward algorithm,Computer science,Artificial intelligence,Mathematical optimization,Markov property,Markov model,Markov chain,Algorithm,Variable-order Markov model,Markov kernel,Hidden Markov model,Machine learning | Conference | 978-0-9824438-4-2 |
Citations | PageRank | References |
3 | 0.48 | 6 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Carsten Fritsche | 1 | 157 | 14.72 |
| Emre Özkan | 2 | 94 | 10.54 |
| Fredrik Gustafsson | 3 | 2287 | 281.33 |