A stochastic approach based on Dynamic Bayesian Networks (DBNs) is introduced for spoken language understanding. DBN-based models allow to infer and then to compose semantic frame-based tree structures from speech transcriptions. Experimental results on the French MEDIA dialog corpus show the appropriateness of the technique which both lead to good tree identification results and can provide th...

The Kilombero Valley floodplain in Tanzania is a major agricultural area. Government initiatives and projects supported by international funding have long sought to boost productivity. Due increasing population pressure, smallholder farmers are forced increase their output. Nevertheless, the level of intensification still lower than what considered necessary production support livelihoods signi...

We propose a novel method to solve a kidnapped robot problem. A mobile robot plans its sensor actions to localize itself using Bayesian network inference. The system differs from traditional methods such as simple Bayesian decision or top-down action selection based on a decision tree. In contrast, we represent the contextual relation between the local sensing results and beliefs about the glob...

The unreliability evaluation of a system including dependencies involving the state of components or the failure events, can be performed by modelling the system as a Dynamic Fault Tree (DFT). The combinatorial technique used to solve standard Fault Trees is not suitable for the analysis of a DFT. The conversion into a Dynamic Bayesian Network (DBN) is a way to analyze a DFT. This paper present...

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...

In this report, we will be interested at Dynamic Bayesian Network (DBNs) as a model that tries to incorporate temporal dimension with uncertainty. We start with basics of DBN where we especially focus in Inference and Learning concepts and algorithms. Then we will present different levels and methods of creating DBNs as well as approaches of incorporating temporal dimension in static Bayesian n...

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...