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You are here		twiecki.io whilemy_mcmc:gently(samples) - The Best Of Both Worlds: Hierarchical Linear Regression in PyMC
\|	\|	austinrochford.com Bayesian Age/Period/Cohort Models in Python with PyMC \| Austin Rochford	6.3 parsecs away Travel
\|	\|	For my day job, I spend a lot of time thinking about e-commerce analytics and cohort analysis in particular. Statistical age-period-cohort (APC) models are important in many fields such as epidemiolo	6.3 parsecs away Travel
\|	\|	austinrochford.com A Modern Introduction to Probabilistic Programming with PyMC \| Austin Rochford	9.0 parsecs away Travel
\|	\|	I first started working with probabilistic programming about ten years ago (in late 2012 or early 2013) using PyMC2. At the time I was preparing to leave a PhD program in pure math for a data science	9.0 parsecs away Travel
\|	\|	austinrochford.com The Hierarchical Regularized Horseshoe Prior in PyMC3 \| Austin Rochford	8.5 parsecs away Travel
\|	\|	For some time I have been interested in better understanding the horseshoe prior1 by implementing it in PyMC3. The horsehoe prior is a continuous alternative to the spike-and-slab prior for sparse Bay	8.5 parsecs away Travel
\|	\|	aosmith.rbind.io Simulate! Simulate! - Part 4: A binomial generalized linear mixed model	78.2 parsecs away Travel
\|		I walk through an example of simulating data from a binomial generalized linear mixed model with a logit link and then exploring estimates of over/underdispersion.	78.2 parsecs away Travel