Probabilistic networks

AL Delcher, S Kasif, HR Goldberg, WH Hsu. Protein secondary structure modelling with probabilistic networks. Intelligent Systems m Molecular Biology 1 109-117, 1993. 

Blau et al. [22] have applied probabilistic network models to model resource needs and success probabilities in pharmaceutical and agrochemical development, through Monte Carlo analysis. This requires solving the problem of scheduling a portfolio of projects under uncertainty about progression. This approach is tractable for drug development. However, the inherent complex-... 

A. Frank and P. Pevzner. PepNovo De novo Peptide Sequencing via Probabilistic Network Modeling. Anal. Chem., 77, no. 4 (2005) 964-973. 

Niculescu, S.P., Kaiser, K.L.E., and Schultz, T.W., Modeling the toxicity of chemicals to Tetmhymena pyri-formis using molecular fragment descriptors and probabilistic networks, Arch. Environ. Contamination Toxicol., 39, 289-298, 2000. 

Friedman N., Murphy K. and Russell S. (1998). Learning the structure of dynamic probabilistic networks. Proceedings of the 14th Conference on the Uncertainty in Artificial Intelligence, pp 139-147. 

Cooper, G. F., and E. Herskovits. 1992. A Bayesian method for the induction of probabilistic networks from data. Machine Learning 9 309-47. 

Chang K.C., Liu X, 1996. Efficient algorithms for learning probabilistic networks . Proceedings of the International Conference on Systems, Man and Cybernetics, IEEE2006, Volume 1. 

Cowell R. G., Dawid A. P., Lauritzen S. L., SpiegeUialter D. J. 1999. Probabilistic Networks and Expert Systems. New York Springer. 

Kjffirul, U. Optimal decomposition of probabilistic networks by simulated annealing. Statistics and Computing 2, 7-17 (1992)... 

In the near future, more sophisticated models can be built using probabilistic networks. A probabilistic network is a factorization of the joint probabiHty function over all the considered variables (markers, interventions, and outcomes) based on knowledge about the dependencies and independencies between the variables. Such knowledge is naturally provided by the hits coming out of the association screen, where each association can be interpreted as a dependency, and the absence of an association as an independency between variables. The model can then be parameterized by fitting to the data, similarly to the linear and logistic regression models, which are in fact special cases of probabilistic network models. 

Using the probabilistic network model, many hypothetical scenarios can be tested in order to... 

Mahboob Straub (2011). This may be a serious limitation for civil engineering applications. In principle event trees can deal with such dependencies however this requires great care during an analysis as observed by Faber Stewart (2003). Moreover both methods suffer from the difficulty in updating based on new information. Petri Nets provide a powerful platform, but the evaluation often takes basis in Monte Carlo simulations requiring considerable computational demands, Nishijima et al. (2009). These drawbacks can be overcome by the use of Bayesian probabilistic networks with discrete nodes supplemented by decision and utility nodes, Nielsen Jensen (2007). 

An important consideration in many learning scenarios is the reliability of data and/or missing values. One learning method that is designed to reason in cases of uncertainty is that of Bayesian probabilistic networks. This method has been used to learn and model reaction mechanisms from physicochemical descriptions of instances of preclassified chemical reactions. ... 

Kanazawa, K., Koller, D., Russell, S. (1995). Stochastic simulation algorithms for dynamic probabilistic networks. In Proceedings of UAl 1995. UAL... 

BBNs are also sometimes called causal probabilistic networks, probabilistic cause-effect models or probabilistic influence diagrams. 

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