Poisson Random Fields for Dynamic Feature Models

Poisson Random Fields for Dynamic Feature Models

Alan Turing Institute via YouTube Direct link

Introduction

1 of 18

1 of 18

Introduction

Class Central Classrooms beta

YouTube playlists curated by Class Central.

Classroom Contents

Poisson Random Fields for Dynamic Feature Models

Automatically move to the next video in the Classroom when playback concludes

  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

Never Stop Learning.

Get personalized course recommendations, track subjects and courses with reminders, and more.

Someone learning on their laptop while sitting on the floor.