Hidden markov models for time series : an introduction using R /
| Main Author: | |
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| Corporate Author: | |
| Other Authors: | , |
| Format: | eBook |
| Language: | English |
| Published: |
Boca Raton :
CRC Press, Taylor & Francis Group,
2016.
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| Edition: | Second edition. |
| Series: | Monographs on statistics and applied probability (Series) ;
150. |
| Subjects: | |
| Online Access: | Connect to the full text of this electronic book |
Table of Contents:
- Preliminaries: mixtures and Markov chains
- Hidden Markov models: definition and properties
- Estimation by direct maximization of the likelihood
- Estimation by the EM algorithm
- Forecasting, decoding and state prediction
- Model selection and checking
- Bayesian inference for Poisson-hidden Markov models
- R packages
- HMMs with general state-dependent distribution
- Covariates and other extra dependencies
- Continuous-valued state processes
- Hidden semi-Markov models and their representation as HMMs
- HMMs for logitudinal data
- Introduction to applications
- Epileptic seizures
- Daily rainfall occurrence
- Eruptions of the Old Faithful geyser
- HMMs for animal movement
- Wind direction at Koeberg
- Models for financial series
- Births at Edendale Hospital
- Homicides and suicides in Cape Town, 1986-1991
- A model for animal behavior which incorporates feedback
- Estimating the survival rates of Soay sheep from makr-recapture-recovery data
- Examples of R code
- Some proofs.