Deodorant is a Clojure package for solving Bayesian optimization (BO) problems
Probabilistic C is a compilation target for probabilistic programming languages.
Java code with Matlab and R examples usage scripts for the sequence memoizer are here.
MATLAB code for a Dirichlet process mixture model with Gaussian likelihood. Includes Gibbs and particle filter estimators. Included is an example script showing how to get started using the code on your own data (the example is a spike sorting, however the code can be used for any data for which which multivariate Gaussian likelihood assumption is appropriate).
This MATLAB code (.zip .tar.gz) was used to generate the results in A nonparametric Bayesian alternative to spike sorting.
Included in this distribution is matlab code to generate posterior samples for linear Gaussian and binary matrix factorization (noisy-or) Indian Buffet Process models. Code similar to this was used to generate the results in Particle filtering for non-parametric Bayesian matrix factorization PDF and A non-parametric Bayesian method for inferring hidden causes PDF. Three different posterior sampling algorithms are provided: Gibbs, reversible jump Markov chain Monte Carlo (RJMCMC), and sequential importance sampling (SIS). Only the Gibbs and SIS samplers are provided for the linear Gaussian IBP models. .tar.gz .zip
Matlab contains a Hypergeometric1F1 function named hypergeom which unfortunately calls underlying maple symbolic routines. These scripts numerically approximate the function value by truncating the underlying series: Hypergeometric1F1.m Hypergeometric1F1Regularized.m
This code implements the methods presented in Inferring attentional state and kinematics from motor cortical firing rates .pdf. In this work a mixed continuous (monkey hand state) and discrete (attentional state) model is defined and a particle filter is used to estimate both simultaneously from firing rates from the same motor cortical population. .zip</dd>