Mathematical Statistics: Markov Processes

<p>An introduction to Markov Processes in discrete and continuous time.</p><p>The course treats: Markov chains: model graphs, Markov property, transition probabilities, persistent and transient states, positive and null persistent states, communication, existence and uniqueness of stationary distribution, and calculation thereof, absorption times.</p> <p>Poisson process: Law of small numbers, counting processes, event distance, non-homogeneous processes, diluting and super positioning, processes on general spaces.</p> <p>Markov processes: transition intensities, time dynamic, existence and uniqueness of stationary distribution, and calculation thereof, birth-death processes, absorption times.</p> <p>Introduction to renewal theory and regenerative processes.</p>