The Factored Frontier (FF) algorithm is a simple approximate inference algorithm for Dynamic Bayesian Networks (DBNs). It is very similar to the fully factorized version of the Boyen-Koller (BK) algorithm, but in­ stead of doing an exact update at every step followed by marginalisation (projection), it always works with factored distributions. Hence it can be applied to models for which the exa...

Systems such as sensor networks and teams of autonomous robots consist of multiple autonomous entities that interact with each other in a distributed, asynchronous manner. These entities need to keep track of the state of the system as it evolves. Asynchronous systems lead to special challenges for monitoring, as nodes must update their beliefs independently of each other and no central coordin...

Consonant duration is influenced by a number of linguistic factors such as the consonant’s identity, within-word position, stress level of the previous and following vowels, phrasal position of the word containing the target consonant, its syllabic position, identity of the previous and following segments. In our work, consonant duration is predicted from a Bayesian belief network (BN) consisti...

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 (dai...

A current popular approach to representing time in Bayesian belief networks is through Dynamic Bayesian Networks (DBNs) (Dean & Kanazawa 1989). DBNs connect sequences of entire Bayes networks, each representing a situation at a snapshot in time. We present an alternative method for incorporating time into Bayesian belief networks that utilizes abstractions of temporal representation. This metho...

Finding the I Most Probable IJxplanations (MPE) of a given evidence, Se, in a Bayesian belief network is a process to identify and order a set of composite hypotheses, His, of which the posterior probabilities are the I largest; i.e., Pr(Hii&) 2 Pr(H21&) 2 . . . L Pr(&ISlz)~ A composite hypothesis is defined as an instantiation of all the non-evidence variables in the network. It could be shown...

Natural and human-made disasters can disrupt infrastructures even if they are designed to be hazard resistant. While the occurrence of hazards only predicted some extent, their impact managed by increasing emergency response reducing vulnerability infrastructure. In context risk management, ability infrastructure withstand damage re-establish initial condition has recently gained prominence. Se...

In this report we present an extension to Continuous Time Bayesian Networks (CTBN) called Generalized Continuous Time Bayesian Networks (GCTBN). The formalism allows one to model, in addition to continuous time delayed variables (with exponentially distributed transition rates), also non delayed or “immediate” variables, which act as standard chance nodes in a Bayesian Network. This allows the ...

Title of dissertation: An Extended Bayesian Belief Network Model of Multi-agent Systems for Supply Chain Management Ye Chen, Doctor of Philosophy, 2001 Dissertation Directed by: Yun Peng, Associate Professor, Department of Computer Science and Electronic Engineering This dissertation develops a theoretical model, called an extended Bayesian Belief Network (eBBN), of a Multi-agent System for Sup...

In this paper, we propose a method to analyse the social correlation among the group of people in any small gathering; such as business meetings, group discussion, etc.; Within such networks, correlation is build based on the tracked facial emotions of all the individuals in the network. The facial emotional feature extraction is based on active appearance model; whereas the approach for emotio...