Stanford open courses

http://pgm-class.org/ --> https://www.coursera.org/pgm/lecture/index <-- Daphne Koller see R code (CRAN) for BN: Bayesian networks - R CRAN for BN Bayesian networks

http://www.universalsubtitles.org/en/teams/coursera/p/pgm

free version

Scribd Sections of the Stanford Class on Bayesian nets

Amara "Universal Subtitles"

Coursera

Probabilistic Graphical Models: Principles and Techniques CS 228 Online Monday March 19 2012.

Open courses

https://www.coursera.org/pgm/lecture/index

1. 1 skip

1. 2 end has outline

1. 3 Intro Motivation and Overview

PGM's Prob graphical models PTM?

CPCS medical 900 edges

Markov

Graphical Rep

high dimensional

methods for explicit reasoning

sparse parameterization

feasible elicitation

superpixels - machine learning to separate superpixels

Many apps

Image segmentation

sick kids

traffic: current and future

Biol Network reconstructions protein levels

discovering interactions

Cover

Representations of PGMs

directed + und

inference

learning

parameters & structure

Homework 1: Bayesian Network Fundamentals

• Robert E. Kass and Adrian E. Raftery. 1995. Bayes Factors Journal of the American Statistical Association , Vol. 90, No. 430 (Jun., 1995), pp. 773-795