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Nov 18, 2024
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STAT 5P89 - Bayesian and Causal Bayesian Networks
Representation (factorizing joint probabilistic distributions, exploiting probabilistic independence properties, d-separation, I-Map, naive Bayes); parameter learning (maximum likelihood estimation, Bayesian parameter estimation); structure learning (constrained-based approaches, score-based approaches, Bayesian model averaging); learning with incomplete data (parameter and structure learning, learning with hidden variables); causal Bayesian networks (intervention, SCMs, inference, learning). Use of R or Python.
Prerequisite(s): STAT 2P81, STAT 3P85, or the permission of the instructor.
Cross-Listing(s): STAT 4P89
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