Visualization

This is a test library to provide reference implementations of MCMC algorithms and ideas. The basis and reference for much of this library is from Michael Betancourt’s wonderful A Conceptual Introduction to Hamiltonian Monte Carlo. The highlight of the library right now is the ~15 line Hamiltonian Monte Carlo implementation (which relies on an 8 line integrator). This is commented and documented, with an aim to be instructive to read.

A library for making ridge plots of… ridges. Choose a location, get an elevation map, and tinker with it to make something beautiful. Heavily inspired from Zach Cole’s beautiful art, Jake Vanderplas’ examples, and Joy Division’s 1979 album “Unknown Pleasures”. Uses matplotlib, SRTM.py, numpy, and scikit-image (for lake detection).

Prototype to interactively visualize Hamiltonian Monte Carlo sampling in javascript

Plot the paths of all your runs from a year. Using Strava.

I helped create ArviZ, a Python package for exploratory analysis of Bayesian models that is compatible with PyStan, PyMC3, emcee, Pyro, and TensorFlow probability. Includes functions for posterior analysis, model checking, comparison and diagnostics. Paper, docs, and on GitHub.

imcmc (im-sea-em-sea) is a small library for turning 2d images into probability distributions and then sampling from them to create images and gifs.

A small script built on top of d3js allowing you to define simple animated parametric equations.