Lecture Notes for Stat 375 Inference in Graphical Models

STAT 566 Fall 2013 Statistical Inference Lecture Notes

2 Lecture 2: Evaluation of Statistical Procedures I 2 2.1 How to compare δ? . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2 2.2 Comparing risk function I: Bayes risk . . . . . . . . . . . . . . . . . . . . . . 3 2.3 Bayes theorem . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3 2.4 Bayes risk revisited . . . . . . . . . . . . . . . . . . . . . . . . . ...

Statistical inference with probabilistic graphical models

These are notes from the lecture of Devavrat Shah given at the autumn school “Statistical Physics, Optimization, Inference, and Message-Passing Algorithms”, that took place in Les Houches, France from Monday September 30th, 2013, till Friday October 11th, 2013. The school was organized by Florent Krzakala from UPMC & ENS Paris, Federico Ricci-Tersenghi from La Sapienza Roma, Lenka Zdeborová fro...

STAT 8630, Mixed-Effect Models and Longitudinal Data Analysis — Lecture Notes

Lecture Seven - STAT 212a

Structure Learning with Nonparametric Decomposable Models

We present a novel approach to structure learning for graphical models. By using nonparametric estimates to model clique densities in decomposable models, both discrete and continuous distributions can be handled in a unified framework. Also, consistency of the underlying probabilistic model is guaranteed. Model selection is based on predictive assessment, with efficient algorithms that allow f...