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...

In this paper, two different methods for information fusion are compared with respect to communication cost. These are the lambda-pi and the junction tree approach as the probability computing methods in Bayesian networks. The analysis is done within the scope of large distributed networks of computing nodes. The result of this comparison enables us to make a statement about the most appropriat...

Recent reports have described that learning Bayesian networks are highly sensitive to the chosen equivalent sample size (ESS) in the Bayesian Dirichlet equivalence uniform (BDeu). This sensitivity often engenders some unstable or undesirable results. This paper describes some asymptotic analyses of BDeu to explain the reasons for the sensitivity and its effects. Furthermore, this paper presents...

Modelling segment duration in text-to-speech systems is hindered by the database imbalance and factor interaction problems. We propose a probabilistic Bayesian belief network (BN) approach to overcome 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 in a concise way of ca...

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 ...

Probabilistic inference and maximum a posteriori (MAP) explanation are two important and related problems on Bayesian belief networks. Both problems are known to be NP-hard for both approximation and exact solution. In 1997, Dagum and Luby showed that efficiently approximating probabilistic inference is possible for belief networks in which all probabilities are bounded away from 0. In this pap...

Integer linear programming (ILP) has long been an important tool for Operations Research akin to our AI search heuristics for NP-hard problems. However, there has been relatively little incentive to use it in AI even though it also deals with optimization. The problem stems from the misperception that because the general ILP problem is diicult to solve, then it will be diicult for all cases. As...

We propose an algorithm called Hybrid Loopy Belief Propagation (HLBP), which extends the Loopy Belief Propagation (LBP) (Murphy et al., 1999) and Nonparametric Belief Propagation (NBP) (Sudderth et al., 2003) algorithms to deal with general hybrid Bayesian networks. The main idea is to represent the LBP messages with mixture of Gaussians and formulate their calculation as Monte Carlo integratio...