NMAA13024U Conditioning and Markov Properties (Beting)

Knowledge:

Basic knowledge of the topics covered by the course: Conditional distributions, conditional independence,  Bayesian networks, and other Markov structures.

Skill:

• Use concepts such as Markov kernels and conditional distributions.
• Compute conditional expectations using conditional distributions.
• Describe and compute the distribution of a system of random variables given a transformation
• Discuss and understand general properties of conditional independence
• Discuss and understand specific examples of graphical Markov models
• Discuss and understand Bayesian networks in concrete examples.
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Competence:

• Discuss the relation between conditional expectations and conditional distributions.
• Understand the concept of conditional independence and relate it distributions relevant to statistical arguments
• Discuss the relation between Markov chains, Bayesian networks, and other Markov structures