Bayesian Network has provided a convenient frame structure to express causal relationship, by regarding influence diagram as a special Bayesian Network, then the value of each decision variable is imposed externally to meet the goals, rather than derive from determination probability of father node. Using influence diagram to describe the node types of directed acyclic graph has been studied, w...

In this paper, we propose an alternative for routing based packet forwarding, which uses network coding to increase transmission efficiency, in terms of both compression and error resilience. This non-adaptive encoding is called quantized network coding, which involves random linear mapping in the real field, followed by quantization to cope with the finite capacity of the links. At the gateway...

Feature Markov Decision Processes (ΦMDPs) [Hut09] are well-suited for learning agents in general environments. Nevertheless, unstructured (Φ)MDPs are limited to relatively simple environments. Structured MDPs like Dynamic Bayesian Networks (DBNs) are used for large-scale realworld problems. In this article I extend ΦMDP to ΦDBN. The primary contribution is to derive a cost criterion that allows...

Pattern recognition using Dynamic Bayesian Networks (DBNs) is currently a growing area of study. In this paper, we present DBN models trained for classification of handwritten digit characters. The structure of these models is partly inferred from the training data of each class of digit before performing parameter learning. Classification results are presented for the four described models.

In this paper, we investigate the use of dynamic Bayesian networks (DBNs) to explicitly represent models of hidden features, such as articulatory or other phonological features, for automatic speech recognition. In previous work using the idea of hidden features, the representation has typically been implicit, relying on a single hidden state to represent a combination of features. We present a...

This paper explores a strategy for determining public space safety. Due to varied purposes and locations, each public space has architecture as well as facilities. A generalized analysis of capacities for public spaces is essential. The method we propose is to examine a public space with a given architecture. We used Bayesian Belief Network to determine the level of safety and identify points o...

This paper proposes a proactive management system for the events that occur across multiple personal user devices, including desktop PCs, laptops, and smart phones. We implemented the Personal Event Management Service using Dynamic Bayesian Networks (PEMS-DBN) system that proactively executes appropriate tasks across multiple devices without explicit user requests by recognizing the user’s devi...

We study dynamic reliability of systems where system components age with a constant failure rate and there is a budget constraint. We develop a methodology to effectively prepare a predictive maintenance plan of such a system using dynamic Bayesian networks (DBNs). DBN representation allows monitoring the system reliability in a given planning horizon and predicting the system state under diffe...

in this research, the decision on belief (dob) approach was employed to analyze and classify the states of uni-variate quality control systems. the concept of dob and its application in decision making problems were introduced, and then a methodology for modeling a statistical quality control problem by dob approach was discussed. for this iterative approach, the belief for a system being out-o...