AMS268: Advanced Bayesian Computation

Teaches some advanced techniques in Bayesian Computation. Topics include Hamiltonian Monte Carlo; slice sampling; sequential Monte Carlo; assumed density filtering; expectation propagation; stochastic gradient descent; approximate Markov chain Monte Carlo; variational inference; and stochastic variational inference. Prerequisite(s): course 207, or by permission of the instructor. Enrollment restricted to graduate students; undergraduates may enroll by permission of the instructor.

5 credits

Year	Fall	Winter	Spring	Summer

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