![]() ![]() 10.1.2 Aggregated binomial: Chimpanzees again, condensed.10.1.1 Logistic regression: Prosocial chimpanzees.9.2.3 Absolute and relative differences.9.2.2 Linking linear models to distributions.9 Big Entropy and the Generalized Linear Model.8.4 Care and feeding of your Markov chain.8.1 Good King Markov and His island kingdom.7.5 Summary Bonus: marginal_effects()/ conditional_effects().7.2 Symmetry of the linear interaction.7.1.4 Interpreting an interaction estimate.7.1.2 Adding a linear interaction does work.7.1.1 Adding a dummy variable doesn’t work.6.4.3 DIC and WAIC as estimates of deviance.6.2 Information theory and model performance.6.1.1 More parameters always improve fit.6 Overfitting, Regularization, and Information Criteria.5.4.4 Another approach: Unique intercepts.5.4.3 Adding regular predictor variables.5.1.3 Plotting multivariate posteriors.4.3.5 Fitting the model with map brm().4.3.3 Grid approximation of the posterior distribution.4.2.1 Re-describing the globe tossing model.4.1 Why normal distributions are normal.3.1 Sampling from a grid-like approximate posterior. ![]()
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