Particle-Based Online Bayesian Learning of Static Parameters with Application to Mixture Models
This thesis investigates the possibility of using Sequential Monte Carlo methods (SMC) to create an online algorithm to infer properties from a dataset, such as unknown model parameters. Statistical inference from data streams tends to be difficult, and this is particularly the case for parametric m...
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| Format: | Others |
| Language: | English |
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KTH, Matematisk statistik
2020
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| Online Access: | http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-279847 |