Methods for Scalable Probabilistic Inference - IPAM at UCLA

Methods for Scalable Probabilistic Inference - IPAM at UCLA

Institute for Pure & Applied Mathematics (IPAM) via YouTube Direct link

Introduction

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1 of 33

Introduction

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Methods for Scalable Probabilistic Inference - IPAM at UCLA

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  1. 1 Introduction
  2. 2 Why Im here
  3. 3 Punchlines
  4. 4 Tools
  5. 5 Integrals
  6. 6 Highdimensional integral
  7. 7 Physical mod
  8. 8 Monte Carlo
  9. 9 Good sampler
  10. 10 Fast probability calculations
  11. 11 There are other answers
  12. 12 Gradients
  13. 13 Higherorder information
  14. 14 Potential energy
  15. 15 Multiple parameters
  16. 16 Integrating a dynamical system
  17. 17 Any questions right now
  18. 18 Any other questions
  19. 19 Derivatives
  20. 20 Hamiltonian Sampling
  21. 21 Automatic differentiation
  22. 22 What is automatic differentiation
  23. 23 Example from my work
  24. 24 Open source tools
  25. 25 Deep learning tools
  26. 26 JAX
  27. 27 Exoplanet
  28. 28 Solarite
  29. 29 Nonstationarity
  30. 30 Scaling linearly
  31. 31 Summary
  32. 32 Documentation
  33. 33 Interfaces

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