Results 1 
4 of
4
Exploring the noisy threshold function in designing bayesian networks
 In Proceedings of SGAI International Conference on Innovative Techniques and Applications of Artificial Intelligence
, 2005
"... Causal independence modelling is a wellknown method both for reducing the size of probability tables and for explaining the underlying mechanisms in Bayesian networks. Many Bayesian network models incorporate causal independence assumptions; however, only the noisy OR and noisy AND, two examples of ..."
Abstract

Cited by 5 (1 self)
 Add to MetaCart
(Show Context)
Causal independence modelling is a wellknown method both for reducing the size of probability tables and for explaining the underlying mechanisms in Bayesian networks. Many Bayesian network models incorporate causal independence assumptions; however, only the noisy OR and noisy AND, two examples of causal independence models, are used in practice. Their underlying assumption that either at least one cause, or all causes together, give rise to an effect, however, seems unnecessarily restrictive. In the present paper a new, more flexible, causal independence model is proposed, based on the Boolean threshold function. A connection is established between conditional probability distributions based on the noisy threshold model and Poisson binomial distributions, and the basic properties of this probability distribution are studied in some depth. The successful application of the noisy threshold model in the refinement of a Bayesian network for the diagnosis and treatment of ventilatorassociated pneumonia demonstrates the practical value of the presented theory. 1
Noisy threshold functions for modelling causal independence in Bayesian networks
, 2006
"... Causal independence modelling is a wellknown method both for reducing the size of probability tables and for explaining the underlying mechanisms in Bayesian networks. Many Bayesian network models incorporate causal independence assumptions; however, only the noisy OR and noisy AND, two examples of ..."
Abstract

Cited by 2 (2 self)
 Add to MetaCart
(Show Context)
Causal independence modelling is a wellknown method both for reducing the size of probability tables and for explaining the underlying mechanisms in Bayesian networks. Many Bayesian network models incorporate causal independence assumptions; however, only the noisy OR and noisy AND, two examples of causal independence models, are used in practice. Their underlying assumption that either at least one cause, or all causes together, give rise to an effect, however, seems unnecessarily restrictive. In the present paper a new, more flexible, causal independence model is proposed, based on the Boolean threshold function. A connection is established between conditional probability distributions based on the noisy threshold model and Poisson binomial distributions, and the basic properties of this probability distribution are studied in some depth. We present and analyse recursive methods as well as approximation and bounding techniques to assess the conditional probabilities in the noisy threshold models.
Modelling treatment effects in a clinical Bayesian network using Boolean threshold functions
, 2008
"... medical decision making; Decisionsupport systems; Bayesian networks; Causal independence models; Boolean threshold functions; Ventilatorassociated pneumonia; Treatment selection Summary Objective: Appropriate antimicrobial treatment of infections in critically ill patients should be started as soo ..."
Abstract
 Add to MetaCart
(Show Context)
medical decision making; Decisionsupport systems; Bayesian networks; Causal independence models; Boolean threshold functions; Ventilatorassociated pneumonia; Treatment selection Summary Objective: Appropriate antimicrobial treatment of infections in critically ill patients should be started as soon as possible, as delay in treatment may reduce a patient’s