Wykladowca: Marek J. Druzdzel
Getting to know each other; organization and overview of the course. Decision making; uncertainty, preferences, and actions; motivation for decision support; decision support systems. Rationality, rational behavior; good decisions vs. good outcomes; foundations of decision-analytic approach to decision support.
A brief overview of useful statistical techniques.
Bayesian networks. Introduction to GeNIe and SMILE?.
Structuring decisions, causality and probability.
Subjective probability, elicitation of probabilities. Canonical probability distributions: Noisy-OR, -MAX, -AND, -MIN, DeMorgan gates. (non)Importance of precision in numerical parameters. Clarity test, sensitivity analysis, value of information.
Learning Bayesian networks/causal discovery.
Model validation techniques.
Risk attitudes. Quantification of preferences. Expected utility theory. Utility elicitation, sensitivity analysis, value of information.
Conflicting objectives: basic techniques, multi-attribute utility functions.
Influence diagrams.
01 Introduction to Bayesian Inference
05 Learning Bayesian Networks and Causal Discovery