Explores how posterior variance changes in Bayesian models, focusing on Poisson-gamma and beta-binomial cases.
Explores whether additional data always reduces posterior variance in Bayesian statistics, with beta-binomial and normal-normal examples.
Explores the tension between population-level statistics and individual experience, questioning the applicability of averages to personal decisions.
Explains how updating an average grade relates to Bayesian statistics and Kalman filtering, using weighted averages and gain.
Explores the evolution from simple logistic regression to modern AI, comparing model complexity, data requirements, and the surprising effectiveness of large neural networks.
Explores Bayesian alternatives to the frequentist t-test for comparing two means, discussing non-parametric and resampling-based approaches.
Explains the Dirichlet distribution as a multivariate extension of the Beta distribution, with applications in Bayesian statistics and regression models.
A tutorial on performing Bayesian proportion tests for categorical survey data using R and the {brms} package.
A deep dive into the Robins and Ritov statistical paradox, exploring a counterexample where subjective Bayesian inference fails and arguing for a resolution.
A technical guide exploring Penalised Complexity (PC) priors for Gaussian process parameters, including theory and derivation.
Explains the differences between Bayesian posterior predictions, linear predictions, and expected predictions using R, brms, and Stan.
A technical blog post discussing penalized complexity priors in Bayesian statistics, focusing on how to set priors that appropriately penalize model complexity.
A technical guide to implementing Bayesian hurdle lognormal and Gaussian regression models in R for analyzing data with many zero values.
Explains how to apply Bayesian thinking and probability to critically analyze news articles and identify underlying biases.
A tutorial on implementing a nearly fully Bayesian causal inference model using inverse probability weights with R, brms, and Stan.
Explores the challenges and a proposed method for combining Bayesian inference with propensity scores and inverse probability weights for causal analysis.
A technical blog post discussing Bayesian priors, sparsity in high-dimensional models, and scale-mixture of normal priors for statistical computation.
A technical guide on using Bayesian multilevel models with R and brms to analyze country-year panel (time-series cross-sectional) data.
A technical guide on calculating posterior predictions and average marginal effects for multilevel Bayesian models using R and brms.
A guide to using Bayesian beta and zero-inflated beta regression models in R to correctly analyze proportion data.
