Writing Better Statistical Programs in R
A while back a friend asked me for advice about speeding up some R code that they’d written. Because they were running an extensive Monte Carlo simulation of a model they’d been developing, the poor performance of their code had become an impediment to their work. After I looked through their code, it was clear [...]
Symbolic Differentiation in Julia
A Brief Introduction to Metaprogramming in Julia In contrast to my previous post, which described one way in which Julia allows (and expects) the programmer to write code that directly employs the atomic operations offered by computers, this post is meant to introduce newcomers to some of Julia’s higher level functions for metaprogramming. To make [...]
Computers are Machines
When people try out Julia for the first time, many of them are worried by the following example: 1 2 3 4 5 6 7 julia> factorial(n) = n == 0 ? 1 : n * factorial(n – 1) julia> factorial(20) 2432902008176640000 julia> factorial(21) -4249290049419214848 If you’re not familiar with computer architecture, this [...]
The State of Statistics in Julia
Updated 12.2.2012: Added sample output based on a suggestion from Stefan Karpinski. Introduction Over the last few weeks, the Julia core team has rolled out a demo version of Julia’s package management system. While the Julia package system is still very much in beta, it nevertheless provides the first plausible way for non-expert users to [...]
Finder Bug in OS X
Four years after I first noticed it, Finder still has a bug in it that causes it to report a negative number of items waiting for deletion:
My New Book: Developing, Deploying and Debugging Multi-Armed Bandit Algorithms
I’m happy to announce that I’ve started writing a new book for O’Reilly, which will focus on teaching readers how to use Multi-Armed Bandit Algorithms to build better websites. My hope is that the book can help web developers build up an intuition for the core conundrum facing anyone who wants to build a successful [...]
Automatic Hyperparameter Tuning Methods
At MSR this week, we had two very good talks on algorithmic methods for tuning the hyperparameters of machine learning models. Selecting appropriate settings for hyperparameters is a constant problem in machine learning, which is somewhat surprising given how much expertise the machine learning community has in optimization theory. I suspect there’s interesting psychological and [...]
Optimization Functions in Julia
Over the last few weeks, I’ve made a concerted effort to develop a basic suite of optimization algorithms for Julia so that Matlab programmers used to using fminunc() and R programmers used to using optim() can start to transition code over to Julia that requires access to simple optimization algorithms like L-BFGS and the Nelder-Mead [...]
Bayesian Nonparametrics in R
On July 25th, I’ll be presenting at the Seattle R Meetup about implementing Bayesian nonparametrics in R. If you’re not sure what Bayesian nonparametric methods are, they’re a family of methods that allow you to fit traditional statistical models, such as mixture models or latent factor models, without having to fully specify the number of [...]
The Great Julia RNG Refactor
Many readers of this blog will know that I’m a big fan of Bayesian methods, in large part because automated inference tools like JAGS allow modelers to focus on the types of structure they want to extract from data rather than worry about the algorithmic details of how they will fit their models to data. [...]