Network representations for reasoning under uncertainty: influence diagrams, belief networks, and Markov networks. Structuring and assessment of decision problems under uncertainty. Learning from evidence. Conditional independence and requisite information. Node reductions. Belief propagation and revision. Simulation. Linear-quadratic-Gaussian decision models and Kalman filters. Dynamic processes. Bayesian meta-analysis. Limited Enrollment. Prerequisite: probability such as MS&E MS&E 220. Recommended: MS&E 152 or MS&E 252.
3 units · Letter or Credit/No Credit
Network representations for reasoning under uncertainty: influence diagrams, belief networks, and Markov networks. Structuring and assessment of decision problems under uncertainty. Learning from evidence. Conditional independence and requisite information. Node reductions. Belief propagation and revision. Simulation. Linear-quadratic-Gaussian decision models and Kalman filters. Dynamic processes. Bayesian meta-analysis. Limited Enrollment. Prerequisite: probability such as MS&E 220. Recommended: 152 or 252.
Offered in Spring 2027 at Stanford University.