Large Scale Inference in Graphical Models

Content

• Variational methods

  • Global variable models (clustering, topics)
  • Variational iterations
  • Bulk synchronous algorithms

• Message passing

  • Parallel updates
  • Graph coloring
  • Parallel Gibbs Sampler
  • Graphlab

• Approximations for Scalable Inference

  • Large local state
  • Large global state
  • Out of core storage
  • Asynchronous scheduling
  • Fast sampling (item ordering, heaps, fast proposals, SIMD)

Supplementary material

See the syllabus for a detailed overview of the topics covered.