Category: pillow lab

PYM code now on GitHub

We’ve just uploaded the first code release of the Pitman-Yor Mixture (PYM) entropy estimator to GitHub. This is code from our recent NIPS paper “Bayesian estimation of discrete entropy with mixtures of stick-breaking priors“. You can find more details in a longer manuscript which recently appeared on the arXiv.

If you have some countable discrete distributions hanging around, you’re sure to have a blast estimating their entropy. You can browse the project page or directly download a zip file of the code.

Two abstracts accepted to Cosyne

Two of my abstracts (together with Memming Park and Jonathan Pillow) were accepted to this year’s Computational and Systems Neuroscience (Cosyne) conference! In “Got a moment or two? Neural models and linear dimensionality reduction,” we propose an extension of the Generalized Linear Model (GLM) framework which integrates well-known methods for neural dimensionality reduction with a parametric model of neural responses. In “Semi-parametric Bayesian entropy estimation for binary spike trains,” we extend the work of our recent NIPS paper by using a simple model of spike counts as the “base measure” for a Dirichlet distribution.

More information soon in a forthcoming update to the Pillow Lab blog, but until then you can check out the abstracts on my publications page.