ODEs, approximate Bayesian inference, and ArviZ: A tour of the new features.
Projects
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minimc
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.
Ridge Map
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).
jshmc
Prototype to interactively visualize Hamiltonian Monte Carlo sampling in javascript
Strava Calendar
Plot the paths of all your runs from a year. Using Strava.
Simulation Based Calibration
A PyMC3 implementation of the algorithms from: Validating Bayesian Inference Algorithms with Simulation-Based Calibration (Talts, Betancourt, Simpson, Vehtari, Gelman).
Hipster Media
A twitter bot inspired by the wonderful @NYT_first_said, this project uses the Media Cloud project to find the earliest mention of a word in major English language newspapers. It uses this to issue a smugly superior tweet.
imcmc
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.
Date Guesser
A library to extract a publication date from a web page, along with a measure of the accuracy. Built with the support and help of the Center for Civic Media at the MIT Media Lab.
Feed Seeker
A library for finding atom, rss, rdf, and xml feeds from web pages. Produced at the mediacloud project. An incremental improvement over feedfinder2, which was itself based on feedfinder, written by Mark Pilgrim, and maintained by Aaron Swartz until his untimely death.
Skample!
A web app for generating samples from a sketched probability distribution function.
PyMC3
I am a contributor to PyMC3, a “Python package for Bayesian statistical modeling and Probabilistic Machine Learning which focuses on advanced Markov chain Monte Carlo and variational fitting algorithms.”
Compare Cross Country Courses
An offshoot of another project, this allows you to compare times between most collegiate cross country courses.
Minimal Machine Learning Visualization Example II
A demonstration of using Flask, React, and d3js to visualize machine learning models. This is a port of a previous project from Angular to React.
Cross Country Predictions
Using hundreds of thousands of historical cross country running results to make predictions about future meets. The page is updated more than weekly during the season.
In a Word
A small project which once a day checks for new words on Futility Closet and keeps a searchable, downloadable list of the curious words it finds there. Click the random button a few times, or make an Anki study deck.
Predicting March Madness
For the second year in a row, I took part in Kaggle’s contest to predict March Madness winners. The code for the actual model is not very expository (get in touch if you are interested), but I also built a friendlier page to query predictions interactively at the link.
Bayesian Updating
An interactive page for demonstrating and experimenting with the beta distribution. Built with AngularJS and d3js.
> TidyTex
A command line utility for automatically compiling $\LaTeX$, and eliminating auxiliary files. Try it out with pip install tidytex.
Parametric Graphs
A small script built on top of d3js allowing you to define simple animated parametric equations.
Linear Regression Demo
A demonstration of linear regression, overfitting, normalization, and regularization. Allows you to choose data from a distribution, and interactively fit polynomials to the data using least squares, ridge, or Lasso regression.