An Implementation of a Method for Computing the Uncertainty in Inferred Probabilities in Belief Networks
This work tackles the issue of quantifying error in probabilistic inferences for AI systems using belief networks, which is an incremental improvement over existing inference algorithms.
The paper addresses the problem of reporting uncertainty in inferred probabilities within belief networks, implementing and comparing two methods—Approximate Propagation and Monte Carlo Integration—to determine variance in these probabilities.
In recent years the belief network has been used increasingly to model systems in Al that must perform uncertain inference. The development of efficient algorithms for probabilistic inference in belief networks has been a focus of much research in AI. Efficient algorithms for certain classes of belief networks have been developed, but the problem of reporting the uncertainty in inferred probabilities has received little attention. A system should not only be capable of reporting the values of inferred probabilities and/or the favorable choices of a decision; it should report the range of possible error in the inferred probabilities and/or choices. Two methods have been developed and implemented for determining the variance in inferred probabilities in belief networks. These methods, the Approximate Propagation Method and the Monte Carlo Integration Method are discussed and compared in this paper.