Chapter 8 Markov chains

Learning Objectives

1. State the essential features of a Markov chain model.
2. State the Chapman-Kolmogorov equations that represent a Markov chain.
3. Calculate the stationary distribution for a Markov chain in simple cases.
4. Describe a system of frequency based experience rating in terms of a Markov chain and describe other simple applications.
5. Describe a time-inhomogeneous Markov chain model and describe simple applications.
6. Demonstrate how Markov chains can be used as a tool for modelling and how they can be simulated.

Theory

TO ADD THEORY ABOUT MARKOV CHAINS HERE

8.1 Features of a Markov chain model

8.2 Chapman-Kolmogorov equations

8.3 Stationary distribution for a Markov chain

8.4 Frequency based experience rating

8.5 Time-inhomogeneous Markov chain model

8.6 Markov chains in modelling

8.6.1 Simulating a Markov chain

R Practice

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