Quap Lab

Write a Bayesian model the way you would write it on paper, paste your data, and fit it, either by a Gaussian at the posterior mode or by Hamiltonian Monte Carlo.

The approach is not mine. The formula-list notation, quadratic approximation as the way in, and the examples this is built around all come from Statistical Rethinking by Richard McElreath. This is an independent browser implementation of ideas that book teaches, written because working through them without an R install seemed useful. If the subject interests you, read the book.

• A formula list with ~ for stochastic and <- for deterministic relations, twelve distributions.
• Posterior summaries with compatibility intervals, plus a grid cross-check that says when the approximation is off.
• Prior predictive and posterior predictive simulation, so you can see what a prior actually believes.
• WAIC and PSIS-LOO with Pareto k diagnostics for comparing models.