Abstract
A time series is a series of observations of a quantity of interest. Markov models are commonly used in applications to take into account the dependence between successive observations. This chapter describes the statistical analysis of different types of Markov models for categorical and continuous time series data, including hidden Markov models and state space models. Several examples are considered to illustrate how likelihood and Bayesian methods can be used for parameter estimation and prediction. Exercises are given at the end.
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Held, L., Sabanés Bové, D. (2020). Markov Models for Time Series Analysis. In: Likelihood and Bayesian Inference. Statistics for Biology and Health. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-60792-3_10
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DOI: https://doi.org/10.1007/978-3-662-60792-3_10
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