Showing posts with label Bayesian Mixer. Show all posts
Showing posts with label Bayesian Mixer. Show all posts
Notes from 4th Bayesian Mixer Meetup
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 shared with us his experience of using Hamiltonian and Sequential Monte Carlo samplers to model ecosystems.
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.
Finally, Jon gave a brief overview on Daft, a nifty Python package for creating graphs, or plate notation.
The agenda had two feature talks by Dominic Steinitz and Volodymyr Kazantsev and a lightning talk by Jon Sedar.
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| Dominic Steinitz: Hamiltonian and Sequential MC samplers to model ecosystems |
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| Volodymyr Kazantsev: Bayesian Model Averaging |
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| Jon Sedar: Easier Plate Notation in Python using Daft |
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
Bayesian
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Bayesian Mixer
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Daft
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Dynamical Systems
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Model Averaging
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python
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R
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Stan
