A guide to comparing survey sample demographics with national population data using R, covering both classical and Bayesian statistical methods.
Explores the intersection of multiple imputation and probabilistic record linkage, proposing a method to sample link sets for robust statistical analysis.
A statistical analysis of estimating a normal distribution using binary (yes/no) predictions from multiple scientists, applied to a temperature forecasting problem.
Explores using sparse linear algebra to speed up Bayesian inference for linear mixed models and generalizations, with a focus on Python/JAX prototyping.
Explores how Large Language Models perform implicit Bayesian inference through in-context learning, connecting exchangeable sequence models to prompt-based learning.
A data scientist's journey from dogmatic Bayesianism to a pragmatic, 'secular' use of Bayesian tools without requiring belief in the model's literal existence.
A statistical analysis discussing the limitations of confidence intervals, using examples from small-area sampling to illustrate their weak properties.
A guide to six statistical methods (frequentist and Bayesian) for comparing group means, with R and Stan code examples.
Explores Bayesian inference when data strongly contradicts prior expectations, analyzing how heavy-tailed priors and likelihoods affect posterior beliefs.
Analyzing the Monty Hall problem, exploring learning strategies and optimal decisions based on observed game history and host behavior.
Explores Bayesian vs. Frequentist approaches to the multiple comparisons problem in statistical inference and data analysis.
Explores the critical difference between frequentist confidence intervals and Bayesian credible regions, arguing why frequentism often fails scientific inquiry.
A practical introduction to the philosophical and practical differences between frequentist and Bayesian statistics, with Python examples.
A critique of a proposal to lower the p-value threshold for statistical significance from 0.05 to 0.005, arguing it addresses symptoms, not root causes.
Authors respond to critique of their computational linguistics paper on analyzing movie characters, discussing interdisciplinary research methods.
