Poisson Random Fields for Dynamic Feature Models

Poisson Random Fields for Dynamic Feature Models

Alan Turing Institute via YouTube Direct link

Comparing Dynamic vs Static Models (NIPS Data) Test-set perplexity

15 of 18

15 of 18

Comparing Dynamic vs Static Models (NIPS Data) Test-set perplexity

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Classroom Contents

Poisson Random Fields for Dynamic Feature Models

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  1. 1 Introduction
  2. 2 Motivating Example
  3. 3 Poisson Random Field Development based on a population genetic model Sawyer and Hartl, 1992
  4. 4 Background: Indian Buffet Process
  5. 5 Background: Beta Process
  6. 6 The Wright-Fisher Model
  7. 7 The Wright-Fisher Diffusion
  8. 8 The Poisson Random Field
  9. 9 Poisson Random Field for Indian Buffet Processes
  10. 10 The WF-IBP model
  11. 11 MCMC inference
  12. 12 Simulated Data with Linear-Gaussian Observation Model
  13. 13 WF-IBP Topic Model
  14. 14 Comparing Dynamic vs Static Models (Simulated Data)
  15. 15 Comparing Dynamic vs Static Models (NIPS Data) Test-set perplexity
  16. 16 NIPS Topic Model
  17. 17 Concluding Remarks
  18. 18 References

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