Number of credits: 6 hp
Examiner: John Noble
Course literature: Bayesian Networks; an Introduction by T. Koski and J. Noble (Wiley, 2009).
- Probabilistic reasoning: Bayes rule, Jeffrey's rule, Pearl's method of Virtual Evidence, Multinomial sampling and the Dirichlet integral.
- Conditional independence, graphs and d- separation, Bayesian networks, Markov equivalence for graph structures.
- Hard evidence, soft evidence, virtual evidence; Jeffrey's rule and Pearl's method.
- Decomposable graphs, junction trees and chain graphs.
- Learning the conditional probability potentials for a given graph structure.
- Learning the graph structure: the Chow-Liu tree, the Minimum Maximum Hill Climbing algorithm.
- Parametrising the network and sensitivity to parameter changes.
- Causality and Intervention Calculus.
- The junction tree and probability updating.
- Factor graphs and the sum product algorithm.
Organisation: 12 lectures, two lectures per week, in HT1.
Examination: 6 written / computer assignments, one per week, distributed during the course.
Prerequisites: Basic probability theory is necessary, some graph theory is helpful.
Last updated: 2014-04-29