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Notes while reading Graphical Models, Exponential Families and Variational Inference by Martin Wainwright and Michael Jordan.

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First discussion

  • definitions and translations between the different graphical models
  • Statement and intuition behind Hammersley–Clifford theorem
  • derivation / intuition behind sum-product algorithm
  • derivation / intuition behind max-product algorithm
  • intuition behind the message-passing updates for junction trees (work out example for small graph w/ cycle)

Second discussion

  • Walk through max-entropy -> exponential family
  • representation of Gaussian as exponential family in two ways
  • multivariate Gaussian in graphical form + Hammersley-Clifford theorem (maybe Schur complements too)
  • first / second moments as sufficient statistics -> parabola polytope -> why are Gaussians not on the boundary?

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