Hi all!

This blog is about the latest addition to my package SBGLM: a sparse linear regression. This was actually the first model I intended to put in the package, but I didn’t had time to finish implementing it during last term.

Hey! I’ve just released a package on Github called SBGLM, which stands for Sparse Bayes Generalized Linear Models. The purpose of this package is mainly a way to store a lot of code that I have rotting in my laptop, which I think is relevant and useful. The models will follow the Bayesian tradition of the “spike-and-slab” prior for sparsity (Mitchell and Beauchamp 1988), so do not expect to see the so called “Bayesian Lasso” here because it doesn’t work. The idea is that, as time permits, I will be adding more models to the package.

It’s crazy how fast this first term went by! But I loved it. The Department of Statistics at UBC is insanely awesome. The people are the best, from students to faculty and staff. The academic environment is very stimulating, especially in my area of interest, with lots of active reading groups and frequent seminars related to Bayesian Statistics, Probabilistic Models and Statistical Learning. We even had the privilege of attending a talk by the expert in Hamiltonian Monte Carlo Michael Betancourt. We also have lots of interaction with people from Frank Wood’s group at the CS department. And finally, the UBC Vancouver campus is so beautiful! Definitely worth a visit if you happen to be in the city (which also deserves to be visited in itself!).

I just released a package on Github called BIDC, which stands for Bayesian Inference for Default Correlations. I started working on this together with my colleague Victor Medina back when I was a financial stability analyst at the Superintendency of Banks and Financial Institutions in Chile. In fact, you can see the slides from our presentation at the 2017 version of SBIF conference here (Biron and Medina 2017). The difference is that, instead of Stan, the method available in BIDC uses a Metropolis-Hastings-within-Gibbs MCMC sampler (Gilks 1996) written entirely in base R, that runs much faster. Currently, it only supports the naive version shown on those slides.