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Classroom Contents
Advances in Distribution Compression - From Kernel Thinning to Stein Thinning
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- 1 Intro
- 2 Motivation: Computational Cardiology
- 3 Distribution Compression
- 4 Problem Setup
- 5 Maximum Mean Discrepancies
- 6 Square-root Kernels
- 7 Kernel Thinning vs. i.i.d. Sampling: Higher Dimensions
- 8 Kernel Thinning vs. Standard MCMC Thinning Posterior inference for systems of ordinary differential equations (ODES) • P-posterior distribution of coupled ODE model parameters given observed data
- 9 Compression with Bias Correction
- 10 Measuring Distance to P
- 11 Stein Thinning Guarantees
- 12 Stein Thinning in Action: Correcting for Burn-in Goodwin model of oscillatory enzymatic control
- 13 Stein Thinning in Action: Correcting for Tempering
- 14 Conclusions
