Reversible Jump MCMC Sampler for Mondrian Processes

I implelemented a reversible jump MCMC sampler [2] for two dimensional Mondrian Processes [1] in R.

Mondrian Processes are a kind of nonparametric Bayesian model for relational learning. The samples drawn from Mondrian Processes are kd-trees. Below is a plot of a sample drawn from a two dimensional Mondrian Process.

As a special case, two dimensional Mondrian Processes can be used for nonparametric Bayesian co-clustering, where the number of co-clusters can be inferred from observed data. I used reversible jump MCMC [3] for Mondrian Process inference.

Usage Example:
Coming soon...

Python Code:

[1] Daniel M. Roy and Yee W. Teh. The Mondrian Process. NIPS. 2008.
[2] Pu Wang, Kathryn B. Laskey, Carlotta Domeniconi and Michael I. Jordan. Nonparametric Bayesian Co-clustering Ensembles. SDM. 2011.
[3] Peter J. Green. Reversible Jump Markov Chain Monte Carlo Computation and Bayesian Model Determination. Biometrika. 82(4):711-732. 1995.