MCMC over Model Structures

Please note: since 2016 the MCMCMS system is distributed as Bims, an easy to install SWI-Prolog package

MCMCMS constracts Markov chains over model structures defined by either DLP (Distributional Logic Programming) programs or Stochastic Logic Programs (SLPs).

The main intuition is that a DLP program can define the space of all possible models and also assign a probability which reflects our prior belief that a particular model explains the data. In addition to composing such a program, the user must provide a function for calculating the likelihood of the data given a model.

MCMCMS was designed to facilitate MCMC experiments over DLPs. It is modular and allows the user to add new models and associated likelihood functions in a simple way. It is written entirely in Prolog and can be run under two systems: YAP and SWI. It was developed under Linux and is unlikely that it will run on radically different operating systems without changes.

To-date we have experimented with building DLPs that construct BNs, RPDAGs (a super-class of BNs), pedigrees and Classification and Regression Trees. The DLP programs and all necessary functions for running experiments over these models are included in the sources.

Since 2016 the MCMCMS system is distributed as Bims, an easy to install SWI-Prolog package

Current version : mcmcms-0_4_3-2012ba12.tgz (or: mcmcms.tgz).
User guide : MCMCMS_uguide.ps.gz, html: MCMCMS_uguide.html html for local install: MCMCMS_uguide_html.tgz
Output files from the tutorial in the user guide : tutorial_out.tgz (Untar and gunzip in directory doc/MCMCMS_guide_html/tutorial/ .)

Publications

Nicos Angelopoulos and Lodewyk Wessels Effective priors over model structures applied to DNA binding assay data .
AIME'11 Workshop on Probabilistic Problem Solving in Biomedicine Bled, Slovenia, July 2011.

Nicos Angelopoulos, Andreas Hadjiprocopis and Malcolm D. Walkinshaw. Learning binding affinity from augmented high throuput screening data. Chapter in "Chemoinformatics and Advanced Machine Learning Perspectives: Complex Computational Methods and Collaborative Techniques". IGI Book Series on Chemoinformatics. Huma Lodhi and Yoshihiro Yamanishi (Eds). 212-234, 2011.

Nicos Angelopoulos, Andreas Hadjiprocopis and Malcolm D. Walkinshaw. Bayesian ligand discovery from high dimensional descriptor data. ACS Journal of Chemical Information and Modeling , 49(6), 1547-1557, 2009.

Nicos Angelopoulos and James Cussens Bayesian learning of Bayesian Networks with informative priors. Special issue on BN learning. Journal of Annals of Mathematics and Artificial Intelligence , 54(1-3), 53-98, 2008.

Nicos Angelopoulos and James Cussens. Tempering for Bayesian C&RTs. Proceedings of the 2nd International Conference on Machine Learning (ICML05), Bonn, 2005.

Nicos Angelopoulos and James Cussens. Exploiting Informative Priors for Bayesian Classification and Regression Trees. Proceedings of the Nineteenth International Joint Conference on Artificial Intelligence , Edinburgh, Scotland, UK, July - August 2005.

Nicos Angelopoulos and James Cussens. Extended stochastic logic programs for informative priors over C&RTs. In Rui Camacho, Ross King, and Ashwin Srinivasan, editors, Proceedings of the work-in-progress track of the Fourteenth International Conference on Inductive Logic Programming (ILP04), pages 7-11, Porto, September 2004.

Nicos Angelopoulos and James Cussens. On the implementation of MCMC proposals over stochastic logic programs. In Colloquium on Implementation of Constraint and LOgic Programming Systems. Satellite workshop to ICLP'04, Saint-Malo, France, 2004.

Nicos Angelopoulos and James Cussens. Markov chain Monte Carlo using tree-based priors on model structure. In Jack Breese and Daphne Koller, editors, Proceedings of the Seventeenth Annual Conference on Uncertainty in Artificial Intelligence (UAI-2001), Seattle, August 2001. Morgan Kaufmann.

Datasets

Acknowledgements

The first phase of the software's development was supported by the EPSRC grant titled: Induction of Stochastic Logic Programs, 01/09/00--31/08/01. The second phase was supported by the EPSRC grant: Stochastic Logic Programs for MCMC, 01/09/03--31/08/05, GR/S30993/01 under their MATHfit program. During 2006-8 the system benefited from a BBSRC SCIBS (Selective Chemical Intervention in Biochemical Systems) initiative project at the biochemistry group at Edinburgh University.
Last update 2013-12-7.