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Identifiability in Discrete State-Space Models
| Content Provider | Scilit |
|---|---|
| Author | Cole, Diana J. |
| Copyright Year | 2020 |
| Description | This is the second chapter of three focusing in identifiability in a specific area. Here, identifiability in discrete state-space models is explored. Comparative to continuous state-space models where the process is monitored in continuous time, some models represent data which is collected at regular discrete time intervals. If so, a discrete state-space model may be appropriate. The chapter starts by introducing discrete state-space models, then considers different methods for checking identifiability. This includes numerical methods, symbolic methods and Bayesian methods. It also discusses the special case of state-space models, hidden Markov models. Book Name: Parameter Redundancy and Identifiability |
| Related Links | https://content.taylorfrancis.com/books/download?dac=C2014-0-38213-0&isbn=9781315120003&doi=10.1201/9781315120003-8&format=pdf |
| Ending Page | 188 |
| Page Count | 20 |
| Starting Page | 169 |
| DOI | 10.1201/9781315120003-8 |
| Language | English |
| Publisher | Informa UK Limited |
| Publisher Date | 2020-05-10 |
| Access Restriction | Open |
| Subject Keyword | Book Name: Parameter Redundancy and Identifiability Automotive Engineering Bayesian Methods Discrete State Space Models |
| Content Type | Text |
| Resource Type | Chapter |
