Probabilistic Network Library

Probabilistic Network Library (PNL) is the general tool for working with graphical models. The library contains high-performance implementation of algorithms for working with Bayesian networks and Markov networks, such as belief propagation and Junction tree inference, maximum likelihood and expectation maximization. The library is aimed at a wide spectrum of graphical models applications including computer vision, pattern recognition, data mining, and decision theory. The PNL core engine will be optimized and parallelized to give maximum performance on Intel® Architectures.

The probabilistic Network theory is one of the most upcoming scientific areas over numerous applications in bioinformatics, genetics, health protection, and in multifarious areas of the computer science. Library contains all the most well-known algorithms of teaching and conclusion, so, undoubtedly, its development will be very good for plentiful researchers, not only in the area of  Probabilistic Network , and, above all, for specialist, which occupied with various applications. 

1. To design, develop and implement scalable parallel versions of existing inference and learning algorithms.

2. To implement new types of probabilistic networks and distributions, inference and learning algorithms for them.

3. To optimize several algorithms from PNL.

4. To provide connection or interface between PNL and some well-known tools of probabilistic modeling.

• Gergel V.P.
• Belov S.A.
• Sysoyev A.V.
• Abrosimova O.N.
• Bader A.A.
• Vinogradov R.V.
• Gergel A.V.
• Labutina A.A.
• Senin A.V.
• Sidorov S.V.
• Tarasov V.A.
• Chernishova E.N.

1. Scalable parallel versions of inference algorithms Junction Tree Inference, Loopy Belief Propagation, Gibbs Sampling and learning algorithm Expectation Maximization Learning was developed and implemented.
2. Support of LIMID and diagnosis networks was added to PNL including inference algorithm for them. SoftMax and Decision Tree distributions were implemented, some inference algorithms were enlarged to support networks with nodes that have these distributions.
3. Some matrix operations were optimized. Junction Tree Inference algorithm was optimized too.
4. Connection with GeNIe and interface with R were implemented.

1. Абросимова О.Н., Белов С.А., Сысоев А.В. Балансировка вычислительной нагрузки для параллельных алгоритмов на вероятностных сетях. Материалы третьего Международного научно-практического семинара “Высокопроизводительные параллельные вычисления на кластерных системах”, 2003, 230-234
2. Chernyshova E.N., Gergel A.V., Sysoyev A.V. Parallelization principles of message passing algorithm for probabilistic networks, VI International Congress on Mathematical Modeling/Book of abstracts, 2004, 38
3. Belov S. Gergel V., Sysoev A., Scalable parallel inference algorithms in probabilistic networks, Preproceedings of UK-Russia Workshop on Proactive Computing, Nizhny Novgorod, February 2005 pp. 5-10

1. III Международный научно-практический семинар “Высокопроизводительные параллельные вычисления на кластерных системах”, 2003, Н. Новгород.
2. VI International Congress on Mathematical Modeling, 2004, N. Novgorod.
3. UK-Rissia Workshop on Proactive Computing, Nizhny Novgorod, February 2005 pp. 5-10

http://www.sourceforge.net/projects/openpnl