Completed
A traditional Gibbs sampler
Class Central Classrooms beta
YouTube videos curated by Class Central.
Classroom Contents
Data Augmentation MCMC for Bayesian Inference from Privatized Data
Automatically move to the next video in the Classroom when playback concludes
- 1 Intro
- 2 Privacy: a challenge in modern data curation
- 3 The U.S. Census Bureau adopts differential privacy
- 4 The mechanism of differential privacy
- 5 Differential privacy: benefits and challenges
- 6 Situating our (statistical x privacy) framework
- 7 Statistical inference from privatized data
- 8 Existing solutions
- 9 A traditional Gibbs sampler
- 10 A general Metropolis-within Gibbs sampler
- 11 Requirements, run time, and efficiency
- 12 Ergodicity of the proposed sampler
- 13 Application: a naïve Bayes classifier
- 14 Simulation setup
- 15 Posterior mean
- 16 Frequentist coverage
- 17 Empirical acceptance rates
- 18 Summary
