The challenge of agility for adopting new business norms creates the need for measuring performance under changing conditions. This study aims to demonstrate the financial and non-financial consequences of implementing different combinations of lean techniques on the business performance. Bayesian Belief Network is used in studying the effects of factors under changing conditions. There are sev...

In this paper we propose a novel method of sensor planning for a mobile robot localization problem. We represent causal relation between local sensing results, actions, and belief of the global localization using a Bayesian network. Initially, the structure of the Bayesian network is learned from the complete data of the environment using K2 algorithm combined with GA (genetic algorithm). In th...

We present a methodology for representing probabilistic relationships in a general-equilibrium economic model. Speciically, we deene a precise mapping from a Bayesian network with binary nodes to a market price system where consumers and producers trade in uncertain propositions. We demonstrate the correspondence between the equilibrium prices of goods in this economy and the probabilities repr...

Quantifying the semantic relevance between questions and their candidate answers is essential to answer detection in social media corpora. In this paper, a deep belief network is proposed to model the semantic relevance for question-answer pairs. Observing the textual similarity between the community-driven questionanswering (cQA) dataset and the forum dataset, we present a novel learning strat...

Uncertainty is classically represented by probability functions, and diagnostic in an environment poised by uncertainty is usually handled through the application of the Bayesian theorem that permits the computation of the posterior probability over the diagnostic categories given the observed data from the prior probability over the same categories. We show here that the whole problem admits a...

“Damned by Faint Praise” is the phenomenon whereby weak positive information leads to a negative change in belief. However, in a Bayesian model of belief revision positive information can seemingly only exert a positive change in belief. We introduce a version of Bayes’ Theorem incorporating the concept of epistemic closure. This reformalization is able to predict the conditions under which a ‘...

This study presents an approach to use Bayesian belief networks in various optimization tasks in resource and environmental management. A belief network is constructed to work parallel to a deterministic model, and it is used to update conditional probabilities associated with different components of the model. The propagation of probabilistic information occurs in two directions in the network...

In this paper, we formulate the stereo matching problem as a Markov network and solve it using Bayesian belief propagation. The stereo Markov network consists of three coupled Markov random fields that model the following: a smooth field for depth/disparity, a line process for depth discontinuity, and a binary process for occlusion. After eliminating the line process and the binary process by i...

Recent research has demonstrated the great capability of deep belief networks for solving a variety of visual recognition tasks. However, primary focus has been on modelling higher level visual features and later stages of visual processing found in the brain. Lower level processes such as those found in the retina have gone ignored. In this paper, we address this issue and demonstrate how the ...