Parameter Learning from Incomplete Data: Summary

• Non-linear optimization problem

• Methods for learning: EM and Gradient Ascent

  • Exploit inference for learning

Difficulties:

• Exploration of a complex likelihood/posterior

  • More missing data ? many more local maxima
  • Cannot represent posterior ? must resort to approximations

• Inference

  • Main computational bottleneck for learning
  • Learning large networks ? exact inference is infeasible ? resort to stochastic simulation or approximate inference  (e.g., see Jordan’s tutorial)

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