Statistics of Stochastic Processes
Objectives
Stochastic processes – Parametric inference
Renewal processes
Poisson processes – Breakpoints
Markov chain – Absorbing states – Hidden Markov model
Markov jump processes – Phylogenetic models
Queues (only for "Statistical Engineering")
Overview of inhomogeneous Markov chains (only for "Statistical Engineering")
Course outline
Define stochastic processes (Markov chain, Poisson process, Markov process with jumps)
Perform basic calculations for Poisson processes and Markov chains, including simple queuing models.
Describe statistical models involving stochastic processes.
Develop frequentist or Bayesian inference methods derived from stochastic process models.
Prerequisites
Probability, inferential statistics, SAS, R (1A)
Markov chain, Bayesian computation, Martingales, Resampling methods (2A))