Wolfram Language application for Bayesian inference and Gaussian process regression.
This is a package that implements the nested sampling algorithm in pretty much the same way as described by John Skilling in his 2006 paper "Nested Sampling for General Bayesian Computation" (Bayesian Analysis, 1, number 4, pp. 833 - 860; available at: https://projecteuclid.org/download/pdf_1/euclid.ba/1340370944).
It also provides some functionality for Markov Chain Monte Carlo sampling (MCMC) based on built-in (but undocumented) functions in the Statistics`MCMC`
context.
A new recently added function called BayesianLinearRegression
provides the Bayesian alternative to Mathematica's LinearModelFit
.
Finally, there is also some code that helps to construct neural networks for quasi-Bayesian regression as explained on the following pages:
SystemOpen[$UserBaseDirectory]
in a notebook)$UserBaseDirectory
/Applications/BayesianInference)You can now load the package by evaluating:
<< BayesianInference`
Alternatively, you can just set the working directory of your notebook to the same directory as example_code.nb
and load the package using the line above (see the initialisation cell in the notebook for an example).
See the example_code.nb
notebook for a general explanation of the code and several examples. Note that the package underwent significant changes since the previous release and most functionality is invoked in a different way than before.
23 November 2018
release1
tag of this package if you prefer/need the old code. I will probably continue to find small bugs to fix and improvements to make in the near future, so there will most likely be more updates to come.24 November 2018
GeometricBrownianMotionProcess
.25 November 2018
26 November 2018
parallelNestedSampling
always generate their own starting points.28 November 2018
30 November 2018
18 December 2018
predictiveDistribution
where you can specify different keys in the 3rd argument to populate the output association with. This is useful when the inputs contain duplicates (such as can happen in the time series regression example in the last section) or when you need to undo a transformation you applied to the independent coordinates.12 July 2019 (version 1.1)
03 September 2019
inferenceObject
where it would show $Failed
for the "GeneratingDistribution"
property.02 October 2019 (version 1.2)
laplacePosteriorFit
.07 Februari 2021 (version 1.4)
replaceFactorials
to facilitate automatic compilation of factorial functions.