Course description: Bayesian approaches to statistical inference: probabilistic modelling, Bayesian inference workflows, approximation of posterior distributions supported by modelling languages, analysis of Bayesian procedures and posterior approximation methods. [3-0-1] Prerequisites: One of MATH_V 302, STAT_V 302, or MATH_V 418, and either STAT_V 305 or STAT_V 460.
Dates offered:
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Session time: 2025 Winter
Term: 2
Instructors: