# Pivoting posteriors

In Stan, when a parameter is declared as an array, the samples/draws data frame will have columns that use the [i] notation to denote the i-th element of the array. For example, suppose we had a model with two parameters – (\lambda_1) and a (\lambda_2). Instead of declaring them individually – e.g. lambda1 and lambda2, respectively – we may declare them as a single lambda array of size 2: parameters { real lambda[2]; } When we sample from that model, we will end up with samples for lambda[1] and lambda[2].

# Ordinary Differential Equations with Stan in R

A tutorial on fitting a Bayesian ODE model with Stan, R, and {cmdstanr} package.

# Probabilistic programming languages for statistical inference

Introduction This post was inspired by a question about JAGS vs BUGS vs Stan: right, that's what got me confused! so they.. do the same thing? @RallidaeRule — Andrew MacDonald 🌈 (@polesasunder) January 10, 2017 Explaining the differences would be too much for Twitter, so I’m just gonna give a quick explanation here. 2020-05-18 update: Coming from a background of statistical inference in the context of academia and research using R, where these have been the prevalent PPLs for quite some time, I admittedly have a bit of a blind spot for PyMC3.

# Mostly-free resources for learning data science

In the past year or two I’ve had several friends approach me about learning statistics because their employer/organization was moving toward a more data-driven approach to decision making. (This brought me a lot of joy.) I firmly believe you don’t actually need a fancy degree and tens of thousands of dollars in tuition debt to be able to engage with data, glean insights, and make inferences from it. And now, thanks to many wonderful statisticians on the Internet, there is now a plethora of freely accessible resources that enable curious minds to learn the art and science of statistics.