The problems of database imbalance and factor interaction make modelling of segment duration in text-to-speech systems a challenging task. We therefore propose a probabilistic Bayesian belief network (BN) approach to tackle data sparsity and factor interaction problems. The belief network approach makes good estimations in cases of missed or incomplete data. Also, it captures factor interaction...

A Bayesain belief network is a graphical representation of the underlying probabilistic relationships of a complex system. These networks are used for reasoning with uncertainty, such as in decision support systems. This requires probabilistic inference with Bayesian belief networks. Simulation schemes for probabilistic inference with Bayesian belief networks offer many advantages over exact in...

Two apparently contrasting theories have been proposed to account for the development of children's theory of mind (ToM): theory-theory and simulation theory. We present a Bayesian framework that rationally integrates both theories for false belief reasoning. This framework exploits two internal models for predicting the belief states of others: one of self and one of others. These internal mod...

Motivation: Several different threads of research have been proposed for modeling and mining temporal data. On the one hand, approaches such as dynamic Bayesian networks (DBNs) provide a formal probabilistic basis to model relationships between time-indexed random variables but these models are intractable to learn in the general case. On the other, algorithms such as frequent episode mining ar...

This paper presents methods for analyzing the topology of a Bayesian belief network created to qualify and quantify the strengths of investigative hypotheses and their supporting digital evidence. The methods, which enable investigators to systematically establish, demonstrate and challenge a Bayesian belief network, help provide a powerful framework for reasoning about digital evidence. The me...

In this paper, we discuss the use of Bayesian belief networks as a tool for enhancing social network analysis. Traditional social network analysis (SNA) primarily uses graph-theoretic algorithms to compute properties of nodes in a network. However, these algorithms assume a degree of completeness and reliability of the social network data, which cannot always be assured. Applying Bayesian belie...

In this paper, we introduce B2DI model that extends BDI model to perform Bayesian inference under uncertainty. For scalability and flexibility purposes, Multiply Sectioned Bayesian Network (MSBN) technology has been selected and adapted to BDI agent reasoning. A belief update mechanism has been defined for agents, whose belief models are connected by public shared beliefs, and the certainty of ...

The aim of the article is to show a stochastic approach for both modelling and optimizing the statistical agent belief in a probability model. Two networks are defined: a decision network D of the agent belief state and a utility network U, presenting the utility structure of the agent belief problem. The agent belief is presented via the following three items (B,D,U), where B is a Bayesian net...

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 ...