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hello — probabilistic "hello world" — inferring parameters of a normal distribution.
gmm — simple Gaussian mixture model. See infergo case studies for a more elaborated model.
adapt — NUTS adaptation on the Gaussian mixture model.
schools - the 8 schools problem.
ppv — inferring pages-per-visit based on a vector of Beta-Bernoulli processes.
mt - probabilistic "hello world" with multiple inference goroutines.

Path	Synopsis
adapt
gmm
model Gaussian mixture	Gaussian mixture
hello
model Inferring parameters of the Normal distribution from observations	Inferring parameters of the Normal distribution from observations
mt
model Inferring parameters of the Normal distribution from observations	Inferring parameters of the Normal distribution from observations
ppv
model Determining the best bandwidth for page-per-visit prediction (http://dtolpin.github.io/posts/session-depth/)	Determining the best bandwidth for page-per-visit prediction (http://dtolpin.github.io/posts/session-depth/)
schools
model The eight schools example as appears in PyStan documentation (and taken from "Bayesian Data Analysis", Section 5.5 by Gelman et al.i): data { int<lower=0> J; // number of schools vector[J] y; // estimated treatment effects vector<lower=0>[J] sigma; // s.e.	The eight schools example as appears in PyStan documentation (and taken from "Bayesian Data Analysis", Section 5.5 by Gelman et al.i): data { int<lower=0> J; // number of schools vector[J] y; // estimated treatment effects vector<lower=0>[J] sigma; // s.e.