Separable Bayesian Networks, or the Influence Model, are dynamic Bayesian Networks in which the conditional probability distribution can be separated into a function of only the marginal distribution of a node’s parents, instead of the joint distributions. We describe the connection between an arbitrary Conditional Probability Table (CPT) and separable systems using linear algebra. We give an a...

the stochastic outcomes and phenomenon of climate are the reason to use probability sciences in climatology. interactions of most of climatic phenomenon as well as justify them are very acceptable based on probability technique. accordingly it is useful in environmental management and planning. a conditional probability is the probability of an event given that another event has occurred e.g. l...

one of the conventional methods for temporary support of tunnels is to use steel sets with shotcrete. the nature of a temporary support system demands a quick installation of its structures. as a result, the spacing between steel sets is not a fixed amount and it can be considered as a random variable. hence, in the reliability analysis of these types of structures, the selection of an appropri...

In this work an N-state Markov Chain model is attempted to validate, which is used to generate an attenuation time series. The model is applicable for estimating the CCDFs (complement cumulative distribution function) of attenuation. The state transition probability parameters of the model is determined from fade slope statistics of attenuation. The conditional probability density function of f...

Abstract The conditional probability formula is supposed to reflect the correct updating of assignments when new information incorporated. Starting from a non-atomic measure, it proved that provides only transformed measure satisfying “minimum requirement” relational assumption. This result applies standard Bayesian parametric model.

this paper proposes a hybrid method to find cumulative distribution function (cdf) of completion time of gert-type networks (gtn) which have no loop and have only exclusive-or nodes. proposed method is cre-ated by combining an analytical transformation with gaussian quadrature formula. also the combined crude monte carlo simulation and combined conditional monte carlo simulation are developed a...

In this talk, the class of multivariate extended skew normal distributions is introduced. The properties of this class of distributions, such as, the moment generating function, probability density function, conditional probability density functions and independence are discussed. The definition of extended noncentral skew chi-square distribution is given. The necessary and sufficient condition...

In this paper a new receptor modelling method is developed to identify and characterise emission sources. The method is an extension of the commonly used conditional probability function (CPF). The CPF approach is extended to the bivariate case to produce a conditional bivariate probability function (CBPF) plot using wind speed as a third variable plotted on the radial axis. The bivariate case ...