Adventures in Practical Population Inference - IPAM at UCLA

Adventures in Practical Population Inference - IPAM at UCLA

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

Gaussian process regression

13 of 17

13 of 17

Gaussian process regression

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Adventures in Practical Population Inference - IPAM at UCLA

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  1. 1 Outline
  2. 2 Definitions
  3. 3 Single event posterior distribution
  4. 4 Selection effects The observation biased likelihood
  5. 5 Integration methods An aside
  6. 6 Analytic integration
  7. 7 Monte Carlo integration Uncertainty
  8. 8 Evaluating the selection function
  9. 9 Putting it together Uncertainties
  10. 10 Density estimation Methods
  11. 11 Scaled Gaussian Mixture Model
  12. 12 Continuous representations Methods
  13. 13 Gaussian process regression
  14. 14 Neural networks
  15. 15 Why don't we just remove the MC integrals?
  16. 16 Comparing observations with predictions
  17. 17 Summary

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