BNPy: Bayesian Nonparametrics in Python
Python implementations of scalable variational learning algorithms, and some MCMC methods, for Bayesian nonparametric clustering models. Current focus is on hierarchical Dirichlet process hidden Markov models, topic models, and mixture models.
- BNPy Python Toolbox (maintained by Michael Hughes, many contributors)
Motion-Based 3D Object Segmentation
Matlab code which implements Monte Carlo learning algorithms for the distance dependent Chinese restaurant process, using motion-based likelihoods to model aligned three-dimensional meshes. See our 2012 NIPS paper.
- ddCRP Mesh Segmentation Matlab Toolbox (written by Soumya Ghosh)
Correlated Nonparametric Topic Models
Matlab code which implements Monte Carlo learning algorithms for the Doubly Correlated Nonparametric Topic Model, as described in our 2011 NIPS paper.
- DCNT Matlab Toolbox (written by Dae Il Kim)
Transformed Dirichlet Process Image Models
Matlab code which implements learning algorithms for the models in our 2008 IJCV paper, Describing Visual Scenes using Transformed Objects and Parts.
Nonparametric Bayesian Hidden Markov Models
Matlab code which implements the blocked Gibbs sampling learning and inference algorithms for our 2008 ICML paper, An HDP-HMM for Systems with State Persistence; our 2008 NIPS paper, Nonparametric Bayesian Learning of Switching Linear Dynamical Systems; and our 2009 NIPS paper, Sharing Features among Dynamical Systems with Beta Processes.
- HDP-HMM and BP-AR-HMM Matlab Toolboxes (written with Emily Fox)
Nonparametric Belief Propagation
Matlab and C++ code which implements the efficient KD-Tree algorithms from our 2003 NIPS paper, Efficient Multiscale Sampling from Products of Gaussian Mixtures. This provides a good starting point for adapting NBP to new application domains.
- Kernel Density Estimation Matlab Toolbox (written by Alex Ihler and Mike Mandel)