Notes from 4th Bayesian Mixer Meetup

(This article was originally published at mages' blog, and syndicated at StatsBlogs.)

Last Tuesday we got together for the 4th Bayesian Mixer Meetup. Product Madness kindly hosted us at their offices in Euston Square. About 50 Bayesians came along; the biggest turn up thus far, including developers of PyMC3 (Peadar Coyle) and Stan (Michael Betancourt).

The agenda had two feature talks by Dominic Steinitz and Volodymyr Kazantsev and a lightning talk by Jon Sedar.

Dominic Steinitz: Hamiltonian and Sequential MC samplers to model ecosystems

Dominic shared with us his experience of using Hamiltonian and Sequential Monte Carlo samplers to model ecosystems.

Volodymyr Kazantsev: Bayesian Model Averaging

Finding the 'best' model was Volodymyr's challenge. He tried various R packages (BMA, BMS and BAS) for Bayesian model averaging, with various degrees of success.

Jon Sedar: Easier Plate Notation in Python using Daft

Finally, Jon gave a brief overview on Daft, a nifty Python package for creating graphs, or plate notation.

Next meeting

The next Bayesian Mixer Meetup meeting is already scheduled for 21 October. We will be back at Cass Business School, with two talks:

• Darren Wilkinson: Hierarchical Bayesian Modelling of Growth Curves inc Stochastic Processes
• Peadar Coyle: Advanced PyMC3

Please comment on the article here: mages' blog

Tags: Bayesian, Bayesian Mixer, Daft, dynamical systems, model averaging, python, R, STAN