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.
Explores practical differences between Bayesian and Frequentist statistical methods using a sci-fi probability problem.
Explores the Bayesian equivalent of a two-sample t-test, questioning traditional assumptions and proposing a model using discrete distributions.
Explains why Monte Carlo simulation is essential for Bayesian hypothesis testing, using A/B testing and election forecasting as examples.
An interactive Shiny app for exploring Bayesian surprise, showing how prior and likelihood tail heaviness affect posterior beliefs.
Explores Bayesian methods for quantifying uncertainty in deep neural networks, moving beyond single-point weight estimates.
