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Introduction to Stochastic Processes: Course Outline

Understand the notion of a Markov chain, and how simple ideas of conditional probability and matrices can be used to give a thorough and effective account of discrete-time Markov chains.

Course Outline

  • Simple random walk.
  • Markov chains: Transition Probability Matrices.
  • First step analysis.
  • Long Behavior of Markov chains.
  • Classification of states.
  • Limit theorem.
  • Reducible Markov chain, and Poisson process.
  • Distribution associated with Poisson processes.
  • Continuous time Markov chain.
  • Birth and death processes with and without absorbing states.
  • Renewal process.

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