Probabilistic reasoning in intelligent systems: networks of plausible inference
Probabilistic reasoning in intelligent systems: networks of plausible inference
“Conditional inter-causally independent” node distributions, a property of “noisy-or” models
Proceedings of the seventh conference (1991) on Uncertainty in artificial intelligence
Analysis and Design of Digital Systems with VHDL
Analysis and Design of Digital Systems with VHDL
Weighing and Integrating Evidence for Stochastic Simulation in Bayesian Networks
UAI '89 Proceedings of the Fifth Annual Conference on Uncertainty in Artificial Intelligence
Efficient stochastic sampling algorithms for bayesian networks
Efficient stochastic sampling algorithms for bayesian networks
On finding effective courses of action in dynamic uncertain situations
On finding effective courses of action in dynamic uncertain situations
Learning Bayesian Networks
Journal of Artificial Intelligence Research
Loopy belief propagation for approximate inference: an empirical study
UAI'99 Proceedings of the Fifteenth conference on Uncertainty in artificial intelligence
On the optimality of solutions of the max-product belief-propagation algorithm in arbitrary graphs
IEEE Transactions on Information Theory
Iterative decoding of compound codes by probability propagation in graphical models
IEEE Journal on Selected Areas in Communications
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This paper presents a heuristic approach to solve the problem of best set of actions determination in Influence Nets. Influence Nets are a special instance of Bayesian Networks that model uncertain situations by connecting a set of desired effects to a set of actionable events through chains of probabilistic cause and effect relationships. Once an Influence Net is specified, a system analyst is often interested in identifying the set of action which has the highest probability of achieving a desired effect. The existing techniques to solve this problem, such as sensitivity analysis and exhaustive search, have limitations of their own. The proposed algorithm, named SAF, attempts to overcome these limitations. The paper also shows that the problem of best set of actions determination can be formulated as an instance of Mixed Integer Non Linear Programming (MINLP). An empirical study is presented that compares the performance of sensitivity analysis, SAF, and MINLP.