Section outline

  • Lecture 1 (25.02.26) - Introduction: notation, terminology, objectives and organization of the course. Markov Chains: transition matrix and transition probabilities (in one or n steps); law of the process. 

    Lecture 2 (26.02.26) - Markovian semigroup and its action on real functions and on distributions. Equivalent formulations of Markov property and strong Markov property. Construction of Markov Chains. Examples: genetic models  (Wright's model); interacting particle systems (voter model).