Probabilistic Programming and Data Structures

  1. Model Development and Validation
    1. Model Specification Strategies
      1. Prior Elicitation
        1. Expert Knowledge Integration
          1. Reference Priors
            1. Sensitivity Analysis
            2. Likelihood Construction
              1. Data Generating Assumptions
                1. Missing Data Handling
                  1. Measurement Error Models
                  2. Hierarchical Model Design
                    1. Partial Pooling
                      1. Shrinkage Effects
                        1. Group-Level Predictors
                        2. Model Parameterization
                          1. Centered vs Non-Centered
                            1. Reparameterization Techniques
                              1. Constraint Handling
                            2. Model Checking and Diagnostics
                              1. Prior Predictive Checks
                                1. Prior Simulation
                                  1. Domain Knowledge Validation
                                  2. Posterior Predictive Checks
                                    1. Test Statistics
                                      1. Graphical Checks
                                        1. Discrepancy Measures
                                        2. Residual Analysis
                                          1. Standardized Residuals
                                            1. Outlier Detection
                                              1. Model Adequacy Assessment
                                              2. Cross-Validation
                                                1. Leave-One-Out Cross-Validation
                                                  1. K-Fold Cross-Validation
                                                    1. Information Criteria Approximation
                                                  2. Model Comparison and Selection
                                                    1. Information Criteria
                                                      1. Widely Applicable Information Criterion
                                                        1. Leave-One-Out Information Criterion
                                                          1. Deviance Information Criterion
                                                          2. Bayes Factors
                                                            1. Marginal Likelihood Computation
                                                              1. Bridge Sampling
                                                                1. Interpretation Guidelines
                                                                2. Model Averaging
                                                                  1. Bayesian Model Averaging
                                                                    1. Stacking Weights
                                                                      1. Pseudo-BMA

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                                                                  4. Probabilistic Programming Languages and Tools

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                                                                  6. Probabilistic Data Structures Theory