Approximate Bayesian Computation via Classification

Computer Science

• Summary Statistic 100%
• Posterior Distribution 100%
• Approximation (Algorithm) 50%
• Simulated Data 50%
• Estimation Error 50%
• Leibler Divergence 50%
• Contrastive Learning 50%
• Asymptotic Normality 50%
• Machine Learning 50%
• Learning System 50%

Keyphrases

• Approximate Bayesian Computation 100%
• Posterior Distribution 33%
• Accept-reject 33%
• Weighting Scheme 16%
• Estimation Error 16%
• Kullback-Leibler Divergence 16%
• Empirical Distribution Function 16%
• Statistical Inference 16%
• Data Comparison 16%
• Stock Market Volatility 16%
• Machine Learning Classifiers 16%
• Asymptotic Normality 16%
• Acceptance Threshold 16%
• Kernel Type 16%
• Exponential Weights 16%
• Contrastive Learning 16%
• Exponential Kernel 16%
• Simulator-based Models 16%
• Volatility Estimation 16%
• Flexible Machines 16%

Mathematics

• Approximate Bayesian Computation 100%
• Real Data 50%
• Posterior Distribution 33%
• Summary Statistic 33%
• Simulated Data 16%
• Kullback-Leibler Divergence 16%
• Inferential Statistics 16%
• Asymptotic Normality 16%
• True Parameter 16%