An augmented sequential MCMC procedure for particle based learning in dynamical systems
Dynamical systems elicited via state space models are systems that consist of two components: a state and a measurement equation model that evolve over time. This paper addresses Bayesian inference of unknown parameters, or parameter learning, of such systems. Particle-based parameter learning metho...
| Main Authors: | Javvad ur Rehman, M., Dass, S.C., Asirvadam, V.S. |
|---|---|
| Format: | Article |
| Institution: | Universiti Teknologi Petronas |
| Record Id / ISBN-0: | utp-eprints.22085 / |
| Published: |
2019
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| Online Access: |
https://www.scopus.com/inward/record.uri?eid=2-s2.0-85061787661&doi=10.1016%2fj.sigpro.2019.02.020&partnerID=40&md5=5a213d1174b4133bc1860e60cd192746 http://eprints.utp.edu.my/22085/ |
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