36-730 Graphical Models and its Applications

6 units, Carnegie Mellon University

Probabilistic graphical models PGMs lie at the intersection of probability and graph theory. Its application to real world problems has served useful in the process of understanding, formulating and solving problems, and in particular as tools for making decisions and calculating the probability of a particular based on often incomplete collections of prior knowledge. This course will introduce the fundamentals of graphical models and probability propagation algorithms demonstrate how to build and model PGMs using R, focusing on DAGs. The aim will be to learn and demonstrate the versatility of PGMs, through applications and methodology, including its use in decision support, causal and temporal problems. Applications will focus on areas of public policy including criminal justice forensic science, healthmedical, environment, etc.

Prerequisites: none listed

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