Jeremie Coullon's blog: posts about programming, statistics, and jazz
This post is the second of a two-part series on early Monte Carlo methods from the 1940s and 1950s. In my previous post I gave an overview of Monte Carlo methods in the 40s and focused on the 1949 conference in Los Angeles. In this post I’ll go over the classic paper Equation of State Calculations by Fast Computing Machines (1953) by Nick Metropolis and co-authors. I’ll give an overview of the…
This post is the first of a two-part series on early Monte Carlo methods from the 1940s and 1950s. Although Monte Carlo methods had been previously hinted at (for example Buffon’s needle), these methods only started getting serious attention in the 1940s with the development of fast computers. In 1949 a conference on Monte Carlo was held in Los Angeles in which the participants - many of them…
A few years ago Bob Carpenter wrote a fantastic post on how why ensemble methods cannot work in high dimensions. He explains how high dimensions proposals that interpolate and extrapolate among samples are unlikely to fall in the typical set, causing the sampler to fail. During my PhD I worked on sampling problems with infinite-dimension parameters (ie: functions) and intractible gradients. After…
JAX allows you to write optimisers and samplers which are really fast if you use the scan or fori_loop functions. However if you write them in this way it’s not obvious how to add progress bar for your algorithm. This post explains how to make a progress bar using Python’s print function as well as using tqdm. After briefly setting up the sampler, we first go over how to create a basic version…
This post goes over 3 ways to write a sampler using JAX. I found that although there are a bunch of tutorials about learning the basics of JAX, it was not clear to me what was the best way to write a sampler in JAX. In particular, how much of the sampler should you write in JAX? Just the log-posterior (or the loss in the case of optimisation), or the entire loop? This blog post tries to answer…
In this post we’re going to implement natural numbers (positive integers) in OCaml to see how we can define numbers from first principle, namely without using OCaml’s built in Integer type. We’ll then write a simple UI so that we have a basic (but inefficient) calculator. You can find all the code for this post on Github.
Markov Chain Monte Carlo (MCMC) is a class of algorithms for sampling from probability distributions. These are very useful algorithms, but it’s easy to go wrong and obtain samples from the wrong probability distribution. What’s more, it won’t be obvious if the sampler fails, so we need ways to check whether it’s working correctly.
The DjangoVerse is a 3D graph of gypsy jazz players around the world. I designed this with Matt Holborn (he got the idea from the Rhizome) and built it using React and Django.