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This is an advanced undergraduate+graduate course on applied stochastic processes, designed for those students who are going to need to use stochastic processes in their research but do not have the measure-theoretic background to take the Math 561-562 sequence. Measure theory is not a prerequisite for this course.
The goal of this course is a good understanding of basic stochastic processes, in particular discrete-time and continuous-time Markov chains, and their applications. The materials covered in this course include the following:
Discrete-time Markov chains: classes, hitting times, absorption probabilities, recurrence and transience, invariant distribution, limiting distribution, reversibility, ergodic theorem, mixing times;
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