# MAI0096

Bayesian Networks/

Bayesianska nätverk

**Number of credits:** 6 hp

**Examiner:** John Noble

**Course literature:** * Bayesian Networks; an Introduction* by T. Koski and J. Noble (Wiley, 2009).

**Course contents:**

- 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.

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Last updated: 2014-04-29