Confidence Inference in Bayesian Networks
This work addresses the need for reliable confidence estimates in Bayesian network inference, which is incremental as it builds on existing sampling methods.
The paper tackles the problem of probabilistic confidence inference in Bayesian networks by presenting two sampling algorithms, AIS-BN-mu and AIS-BN-sigma, which guarantee that posterior probability estimates are within a desired precision bound with a given probability, showing excellent performance even for very unlikely evidence.
We present two sampling algorithms for probabilistic confidence inference in Bayesian networks. These two algorithms (we call them AIS-BN-mu and AIS-BN-sigma algorithms) guarantee that estimates of posterior probabilities are with a given probability within a desired precision bound. Our algorithms are based on recent advances in sampling algorithms for (1) estimating the mean of bounded random variables and (2) adaptive importance sampling in Bayesian networks. In addition to a simple stopping rule for sampling that they provide, the AIS-BN-mu and AIS-BN-sigma algorithms are capable of guiding the learning process in the AIS-BN algorithm. An empirical evaluation of the proposed algorithms shows excellent performance, even for very unlikely evidence.