An analysis of the effects of particle amount on the accuracy of particle filter estimations of marginal likelihood
Particle filters are a type of genetic Monte Carlo algorithms that are broadly applied on filtering problems arising in signal processing and Bayesian statistical inference. These type of inference problems are easily modelled as hidden Markov models. Particle filters utilize samples, also known as...
| Main Authors: | , |
|---|---|
| Format: | Others |
| Language: | English |
| Published: |
KTH, Skolan för elektroteknik och datavetenskap (EECS)
2018
|
| Subjects: | |
| Online Access: | http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-229772 |