Notes from 4th Bayesian Mixer Meetup

[This article was first published on mages' blog, and kindly contributed to R-bloggers]. (You can report issue about the content on this page here)
Want to share your content on R-bloggers? click here if you have a blog, or here if you don't.

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

To leave a comment for the author, please follow the link and comment on their blog: mages' blog.

R-bloggers.com offers daily e-mail updates about R news and tutorials about learning R and many other topics. Click here if you're looking to post or find an R/data-science job.
Want to share your content on R-bloggers? click here if you have a blog, or here if you don't.

Never miss an update!
Subscribe to R-bloggers to receive
e-mails with the latest R posts.
(You will not see this message again.)

Click here to close (This popup will not appear again)