Bayesian network is a strong tool for uncertain knowledge representation and inference. This paper mainly introduces some technologies and methods about Bayesian network based on intelligent system. In the construction of Bayesian network, divorcing technology and noisy-or technology are used. In the inference of Bayesian network, VE algorithm and sampling algorithm are introduced. Finally, Bay...

Evaporation phenomena is a effective climate component on water resources management and has special importance in agriculture. In this paper, Bayesian belief networks (BBNs) as a non-linear modeling technique provide an evaporation estimation  method under uncertainty. As a case study, we estimated the surface water evaporation of the Persian Gulf and worked with a dataset of observations ...

Evaporation phenomena is a effective climate component on water resources management and has special importance in agriculture. In this paper, Bayesian belief networks (BBNs) as a non-linear modeling technique provide an evaporation estimation  method under uncertainty. As a case study, we estimated the surface water evaporation of the Persian Gulf and worked with a dataset of observations ...

In this paper we present a novel induction algorithm for Bayesian networks. This selective Bayesian network classiier selects a subset of attributes that maximizes predictive accuracy prior to the network learning phase, thereby learning Bayesian networks with a bias for small, high-predictive-accuracy networks. We compare the performance of this classiier with selective and non-selective naive...

more often clay matrix is the major factor to reduce the porosity and permeability in sandstone facies. consequently determination of clay minerals is of prime importance in reservoir quality assessment. the present study aims to identify four different types of clay mineral namely kaolinite, illite/cholorite, halloysite, and montmorilonite from petrophysical logs (pls) using cation exchange ca...

In this paper, we describe how a stochastic PERT network can be formulated as a Bayesian network. We approximate such PERT Bayesian network by mixtures of Gaussians hybrid Bayesian networks. Since there exists algorithms for solving mixtures of Gaussians hybrid Bayesian networks exactly, we can use these algorithms to make inferences in PERT Bayesian networks.

Continuous time Bayesian network classifiers are designed for analyzing multivariate streaming data when time duration of events matters. New continuous time Bayesian network classifiers are introduced while their conditional log-likelihood scoring function is developed. A learning algorithm, combining conditional log-likelihood with Bayesian parameter estimation is developed. Classification ac...

Bayesian networks are normally given one of two types of foundations: they are either treated purely formally as an abstract way of representing probability functions , or they are interpreted, with some causal interpretation given to the graph in a network and some standard interpretation of probability given to the probabilities specified in the network. In this chapter I argue that current f...

Estimating the final price of products is of great importance. For manufacturing companies proposing a final price is only possible after the design process over. These companies propose an approximate initial price of the required products to the customers for which some of time and money is required. Here using the existing data of already designed transformers and utilizing the bayesian anal...