# Author Archives: mrocklin

## GSoC Mentor Summit

I’m in the airport waiting for my flight after finishing the Google Summer of Code Mentor Summit. This event took place this weekend. Two or three mentors from many of the GSoC projects came out to the Google campus to … Continue reading

## Simplifying Sets

SymPy’s sets module is a pleasure to work on. The math is approachable well structured. There are basic sets (Intervals, FiniteSets) compound sets (Unions, Intersections, Cartesian Products) and operations (contains, complement, measure, subset). Because the problem is easy to understand … Continue reading

## sympy.stats is in

Development It seems there was a flurry of development over the winter holidays. Tom’s Meijer-G integration code was merged into master giving SymPy an incredibly powerful definite integration engine. This encouraged me to finish up the pull request for random … Continue reading

## Week 12: Pull Requests

As before, not much to report. Slow plodding through testing, bug fixing, etc…. I have a pull request here for Matrix Expressions https://github.com/sympy/sympy/pull/532 My branch for Finite and Continuous Random Variables is below. It doesn’t have a pull request yet … Continue reading

## Week 11: Testing, cleaning

I’m increasing testing coverage and fixing errors in my random variables branch. I’m not sure how to proceed with the matrix expressions ideas. On one hand I should wait until the community comes to a consensus about what SymPy Matrix … Continue reading

## Week 10 for Random Variables

I’ve been neglecting my GSoC project this week. This is what’s on the burner though: Write up a blogpost on my implementation of Matrix Expressions. What they can and can’t do. I’d like to generate discussion on this topic. Test … Continue reading

## Multivariate Normal Random Variables

Multivariate Normal Random Variables are extraordinarily convenient. The probability density of a multivariate normal random variable is proportional to the following: Where is an n-dimensional state vector, is the mean of the distribution, and is an n by n covariance … Continue reading