University at Buffalo 3 credits Mathematics
Stochastic processes describe the dynamics of systems which evolve in time according to probabilistic laws, rather than deterministic laws. We particularly study Markov processes which have the property that the future only depends on the present, not on the past. Topics to be covered are discrete Markov chains, Poisson processes, continuous Markov chains, Brownian motion, and possibly other topics. The course emphasizes concepts, applications, and computations, rather than rigorous proofs. In particular, measure theory is not employed. This course is dual listed with MTH 546. Prerequisites: MTH 241 and STA 301 or MTH 411.
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