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Requirements, run time, and efficiency
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Classroom Contents
Data Augmentation MCMC for Bayesian Inference from Privatized Data
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- 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
