Confidence Inference in Bayesian Networks

01/10/2013
by   Jian Cheng, et al.
0

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.

READ FULL TEXT

page 1

page 2

page 3

page 4

research
10/19/2012

An Importance Sampling Algorithm Based on Evidence Pre-propagation

Precision achieved by stochastic sampling algorithms for Bayesian networ...
research
03/27/2013

Weighing and Integrating Evidence for Stochastic Simulation in Bayesian Networks

Stochastic simulation approaches perform probabilistic inference in Baye...
research
12/08/2020

Adaptive Sampling for Estimating Distributions: A Bayesian Upper Confidence Bound Approach

The problem of adaptive sampling for estimating probability mass functio...
research
02/14/2012

Measuring the Hardness of Stochastic Sampling on Bayesian Networks with Deterministic Causalities: the k-Test

Approximate Bayesian inference is NP-hard. Dagum and Luby defined the Lo...
research
03/25/2015

An Experiment on Using Bayesian Networks for Process Mining

Process mining is a technique that performs an automatic analysis of bus...
research
06/01/2011

AIS-BN: An Adaptive Importance Sampling Algorithm for Evidential Reasoning in Large Bayesian Networks

Stochastic sampling algorithms, while an attractive alternative to exact...
research
10/19/2012

An Empirical Study of w-Cutset Sampling for Bayesian Networks

The paper studies empirically the time-space trade-off between sampling ...

Please sign up or login with your details

Forgot password? Click here to reset