Posts filed under ‘Machine Learning’

Software for Flexible Bayesian Modeling – New release

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).
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2020-02-17 at 4:06 pm 2 comments

Automatic differentiation in pqR

I’ve released a version of my pqR implementation of R that has extensions for automatic differentiation. This is not a stable release, but it can be downloaded from pqR-project.org — look for the test version at the bottom — and installed the same as other pqR versions (from source, so you’ll need C and Fortran compilers).

Note that this version very likely has various bugs — mostly showing up only if you use automatic differentiation, I hope.

You can read about the automatic differentation facilities here, or with help(Gradient) after installing the test version. Below are a few examples to show a bit of what you can do.
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2019-07-06 at 5:39 pm 1 comment


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