Appendix
Chapter Outline
A1. RELR Maximum Entropy Formulation
A1.1.2. Quadratic, Cubic, and Quartic Constraints
A1.2. Symmetrical Error Probability Constraints on Cross-product Sums
A1.4. RELR and Feature Reduction
A2. Derivation of RELR Logit from Errors-in-Variables Considerations
A3. Methodology for Pew 2004 Election Weekend Model Study
A4. Derivation of Posterior Probabilities in RELR's Sequential Online Learning
A5. Chain Rule Derivation of Explicit RELR Feature Importance
A6. Further Details on the Explicit RELR Low Birth Weight Model in Chapter 3
A7. Zero Intercepts in Perfectly Balanced Stratified Samples
A8. Detailed Steps in RELR's Causal Machine Learning Method ...
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