Friday, March 31, 2006

Graphical Models class notes

I needed to refresh some Graphical Models basics, and here are lecture notes I found useful
  • Jeff Bilmes' Graphical Models course , gives detailed introduction on Lauritzen's results, proof of Hammersley-Clifford results (look in "old scribes" section for notes)
  • Kevin Murphy's Graphical Models course , good description of I-maps
  • Sam Roweis' Graphical Models course , good introduction on exponential families
  • Stephen Wainwright's Graphical Models class, exponential families, variational derivation of inference
  • Michael Jordan's Graphical Models course, his book seems to be the most popular for this type of class
  • Lauritzen's Graphical Models and Inference class , also his Statistical Inference class , useful facts on exponential families, information on log-linear models, decomposability
  • Donna Precup's class , some stuff on structure learning
Additionally, for information on directed/undirected graph equivalence classes, this paper by Meek is good, also this thesis gives some information for equivalence classes in ancestral/chain graphs

8 comments:

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mimimi said...

Graphische Modelle sind ein wichtiger Bestandteil der modernen Statistik und des maschinellen Lernens. Sie helfen dabei, komplexe Zusammenhänge zwischen Variablen visuell darzustellen und zu analysieren. Für viele Studierende bieten die Vorlesungsnotizen zu diesem Thema eine wertvolle Ressource. Dennoch könnten einige behaupten, dass der Versuch, komplizierte mathematische Konzepte grafisch darzustellen, eine dumme Erfindung ist, da sie die Einfachheit der Berechnungen beeinträchtigen könnte. Für andere ist es jedoch eine nützliche Lernhilfe.

NadiaMoore said...

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