Wetlands are valuable natural capital and sensitive ecosystems facing significant risks from anthropogenic climatic stressors. An assessment of the environmental risk levels for wetlands’ dynamic can provide a better understanding their current ecosystem health functions. Different defined by considering categories probability severity each in environment. Determining provides general overview ...

Precision achieved by stochastic sampling algorithms for Bayesian networks typically deteriorates in the face of extremely unlikely evidence. In addressing this problem, importance sampling algorithms seem to be most successful. We discuss the principles underlying the importance sampling algorithms in Bayesian networks. After that, we describe Evidence Pre-propagation Importance Sampling (EPIS...

Bayesian belief networks provide a natural, efficient method for representing probabilistic dependencies among a set of variables. For these reasons, numerous researchers are exploring the use of belief networks as a knowledge representation m artificial intelligence. Algorithms have been developed previously for efficient probabilistic inference using special classes of belief networks. More g...

The Recurrence Local Computation Method (RLCM) for nding the most probable explanations (MPE) in a Bayesian belief network is valuable in assisting human beings to explain the possible causes of a set of evidences. However, RLCM works only on singly connected belief networks. This paper presents an extension of the RLCM which can be applied to multiply connected belief networks for nding arbitr...

We present the use of Bayesian belief networks to formalise reasoning about software dependability, so as to make assessments easier to build and to check. Bayesian belief networks include a graphical representation of the structure of a complex argument, and a sound calculus for representing probabilistic information and updating it with new observations. We illustrate the method and show its ...

The representation of ignorance is a long standing challenge for researchers in probability and decision theory. During the past decade, Artiicial Intelligence researchers have developed a class of reasoning systems, called Truth Maintenance Systems, which are able to reason on the basis of incomplete information. In this paper we will describe a new method for dealing with partially speciied p...

When using Bayesian networks for modelling the behavior of man-made machinery, it usu­ ally happens that a large part of the model is deterministic. For such Bayesian networks the deterministic part of the model can be represented as a Boolean function, and a cen­ tral part of belief updating reduces to the task of calculating the number of satisfying configurations in a Boolean function. In th...

Bayesian learning of belief networks (BLN) is a method for automatically constructing belief networks (ENs) from data using search and Bayesian scoring techniques. K2 is a particular instantiation of the method that implements a greedy search strategy. To evaluate the accuracy of K2, we randomly generated a number of BNs and for each of those we simulated data sets. K2 was then used to induce t...

Bayesian learning of belief networks (BLN) is a method for automatically constructing belief networks (BNs) from data using search and Bayesian scoring techniques. K2 is a particular iustantiation of the method that implements a greedy search strategy. To evaluate the accuracy of K2, we randomly generated a number of BNs and for each of those we simulated data sets. K2 was then used to induce t...