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