Software for Flexible Bayesian Modeling – New release
2020-02-17 at 4:06 pm 2 comments
I’ve released a new version of my Software for Flexible Bayesian Modeling and Markov Chain Sampling (FBM). This is the first public release since 2004, with the first release of the precursor software being in 1995. There was a version mostly completed in 2007 that never got released (due to my not getting around to checking that I’d fixed up everything). The new version has the changes from 2007 plus some more recent updates, including new features used for the tests in this paper.
FBM implements several general-purpose Markov chain sampling methods, such as Metropolis updates, Hamiltonian (Hybrid) Monte Carlo, and slice sampling. These methods can be applied to distributions defined by simple formulas, including posterior distributions for simple Bayesian models. Several more specialized modules have been written that implement posterior distributions for more complex models, including Bayesian neural networks, Gaussian process models, and mixture models (including Dirichlet process mixture models).
Compared to the version from 2004, additions and improvements include:
- A more user-friendly syntax for specifying neural network architectures.
- A somewhat rudimentary module for simulating molecular systems with the Lennard-Jones potential, in NVT and NPT ensembles. This is meant to support research into how well MCMC methods work in this application area.
- A module implementing a specialized Bayesian model for inferring the location of sources of atmospheric contamination.
- An ‘slevel’ Markov chain operation, that supports updating the Uniform(0,1) value used to make accept/reject decisions, as part of the Markov chain state.
- Numerous detailed improvements and bug fixes.
One thing to note is that the computation times in the examples are for a long-obsolete computer. Compute times on a modern desktop computer will likely be smaller by a factor of ten or more.
This software comes with extensive documentation, including tutorial examples, which may be accessed here.
Entry filed under: Machine Learning, Monte Carlo Methods, Statistics, Statistics - Computing.
Radford Neal's blog 
1. Longhai Li | 2020-03-07 at 2:06 pm
It would be a good idea that FBM is installed in compute canada machine then the executables are made publicly available.
2. Radford Neal | 2020-03-07 at 3:46 pm
I’ve never tried using compute canada. So I know nothing about it.
The FBM software is designed for use on a Linux/Unix system (including macs), with interaction via shell commands. On most such systems, build tools are available by default or easily installed, so it shouldn’t be hard to build the software.