An Overview of Bayesian Network Applications in Uncertain Domains <s> I. INTRODUCTION <s> Keywords: qualite ; theorie des erreurs Reference Record created on 2005-06-20, modified on 2016-08-08 <s> BIB001 </s> An Overview of Bayesian Network Applications in Uncertain Domains <s> I. INTRODUCTION <s> This paper presents an efficient computational method for performing sensitivity analysis in discrete Bayesian networks. The method exploits the structure of conditional probabilities of a target node given the evidence. First, the set of parameters which is relevant to the calculation of the conditional probabilities of the target node is identified. Next, this set is reduced by removing those combinations of the parameters which either contradict the available evidence or are incompatible. Finally, using the canonical components associated with the resulting subset of parameters, the desired conditional probabilities are obtained. In this way, an important saving in the calculations is achieved. The proposed method can also be used to compute exact upper and lower bounds for the conditional probabilities, hence a sensitivity analysis can be easily performed. Examples are used to illustrate the proposed methodology. <s> BIB002 </s> An Overview of Bayesian Network Applications in Uncertain Domains <s> I. INTRODUCTION <s> Integrated Assessment (IA) aims to facilitate decision-making processes on complex issues. Dealing with uncertainty is principally at the core of Integrated Assessment. IA practitioners realise that. However: – Not all uncertainties can be adequately addressed with existingmethods and tools. This especially holds for uncertainty in model structure and uncertainty due to behavioural and societal variability, value diversity, technological surprise, ignorance and indeterminacy. – Uncertainty is usually treated as a marginal issue, as an additional physical variable, as a mathematical artifact. The current methods merely involve evaluation of the impacts of `certain uncertainties', i.e. uncertainties for which estimates or probability distributions are available.– Current methods give no indication of the magnitude and sources of thevarious underlying uncertainties and the aggregated uncertainty measures are difficult to understand to decision-makers and other audiences.Within the International Centre for Integrative Studies, Maastricht University (the Netherlands), the research project `Perspectives on uncertainty and risk' aims to develop alternative, complementary approaches to uncertainty management in Integrated Assessment. The current paper summarises the theoretical analysis of the concept of uncertainty, and it proposes a typology of sources of uncertainty. This paper further discusses insights and experiences with pluralistic uncertainty management in thecontext of Integrated Assessment modelling in order to assess the strengths and weaknesses of such an approach. Although systematic fundamental uncertainty research is still needed in the context of Integrated Assessment, this paper argues that significant profit in quality terms can be gained by using the available methods, including the pluralistic approach, more consciously and systematically. <s> BIB003 </s> An Overview of Bayesian Network Applications in Uncertain Domains <s> I. INTRODUCTION <s> In this paper, we address the problem of scene modeling for performing video surveillance. The problem consists of using the trajectories, obtained by observing objects in a scene, to construct a scene model that can be used to distinguish a normal and an acceptable behavior from a atypical one. In this regard, the proposed method is divided into a training phase and a testing phase. During the training phase, the input trajectories are used to identify different paths or routes commonly taken by the objects in a scene. Important discriminative features are then extracted from these identified paths to learn a dynamic Bayesian network (DBN). During the testing phase, the learned network is used to classify the incoming trajectories based on their size, location, speed, acceleration, and spatio-temproal curvature characteristics. The proposed method (i) handles trajectories of varying lengths, (ii) automatically detects the number of paths presents in a scene, and (iii) introduces the novel usage of the DBN, which is very intuitive and accurately captures the dynamics of the scene. We show results on four datasets of varying lengths and successfully show results for both path clustering and anomalous behavior detection. <s> BIB004 </s> An Overview of Bayesian Network Applications in Uncertain Domains <s> I. INTRODUCTION <s> In the privacy preservation of association rules, sensitivity analysis should be reported after the quantification of items in terms of their occurrence. The traditional methodologies, used for preserving confidentiality of association rules, are based on the assumptions while safeguarding susceptible information rather than recognition of insightful items. Therefore, it is time to go one step ahead in order to remove such assumptions in the protection of responsive information especially in XML association rule mining. Thus, we focus on this central and highly researched area in terms of generating XML association rule mining without arguing on the disclosure risks involvement in such mining process. Hence, we described the identification of susceptible items in order to hide the confidential information through a supervised learning technique. These susceptible items show the high dependency on other items that are measured in terms of statistical significance with Bayesian Network. Thus, we proposed two methodologies based on items probabilistic occurrence and mode of items. Additionally, all this information is modeled and named PPDM (Privacy Preservation in Data Mining) model for XARs. Furthermore, the PPDM model is helpful for sharing markets information among competitors with a lower chance of generating monopoly. Finally, PPDM model introduces great accuracy in computing sensitivity of items and opens new dimensions to the academia for the standardization of such NP-hard problems. <s> BIB005 </s> An Overview of Bayesian Network Applications in Uncertain Domains <s> I. INTRODUCTION <s> Identifying interactions among brain regions from structural magnetic-resonance images presents one of the major challenges in computational neuroanatomy. We propose a Bayesian data-mining approach to the detection of longitudinal morphological changes in the human brain. Our method uses a dynamic Bayesian network to represent evolving inter-regional dependencies. The major advantage of dynamic Bayesian network modeling is that it can represent complicated interactions among temporal processes. We validated our approach by analyzing a simulated atrophy study, and found that this approach requires only a small number of samples to detect the ground-truth temporal model. We further applied dynamic Bayesian network modeling to a longitudinal study of normal aging and mild cognitive impairment — the Baltimore Longitudinal Study of Aging. We found that interactions among regional volume-change rates for the mild cognitive impairment group are different from those for the normal-aging group. <s> BIB006
Uncertainty is a commonly faced problem in real world applications. Uncertainty can be described as an inadequate amount of information BIB001 . Nevertheless, uncertainty may also exist in situations that have enough amount of information BIB003 . Furthermore, uncertainty may be alleviated or eliminated with the addition of new information. Addition of more information in complex processes may lead to mining of limited knowledge. Uncertainty can be computed mathematically with probability theory. In uncertain situations, there is an involvement of possibility of states of attributes. Consequently, the models established on probabilistic inferences have the capability to assign a probabilistic value according to a defined principle. Accordingly, the prediction with large number of states in a model is accomplished. The question rises "how prediction is realized in the presence of large number of states in a model?" An answer to this question is the employment of Bayesian Network (BN) with several variables BIB002 -  . BNs, also known as belief networks, belong to the family of probabilistic graphical models. These graphical structures correspond to knowledge about an uncertain domain. More specifically, each node in the graphical structure represents a random variable, while the edges/arcs between the nodes represent conditional dependencies among nodes. These conditional dependencies are estimated by using acknowledged statistical and computational methods. Consequently, BNs incorporate concepts from graph and probability theory, computer science, and statistics. Since last two decades, BN is recognized as an important tool for a number of expert systems especially in domains involving uncertainty  . This recognition of BN has several reasons behind it. First, BN encodes the dependencies of entire set of variables transparently, even if some data is missing. Second, causal links can be shown in a graphical form with variables which can easily be comprehended by humans. Third, BN can be used to build a bond between data and the prior knowledge in order to draw observations based conclusions. Lastly, BN, in conjunction with statistical methods, can be used to overcome data over fitting problem. In this way, the mathematical precision with probabilistic inferences is guaranteed with the help of BN in domains involving uncertainty. Several domains are identified by various researchers in terms of their application, learning or integration with BN and some of them include Image Processing BIB006 - BIB004 System Reliability Analysis and PPDM (Privacy Preserving in Data Mining)  - BIB005 , Medical Diagnosis  -  and Decision Support  -  . In this study, we have explored applications of BN in a variety of domains having uncertainty. We have presented literature on BN based approaches in these domains. The objective is to highlight the importance of BN with detailed literature in different research domains. Moreover, the strengths and limitations of these approaches have also been reviewed. The remainder of the paper is organized as follows. In Section II, the theoretical concepts and mathematical modeling of Bayesian Network is presented to support a better understanding of subsequent sections. In Section III to Section VII, an overview of application of BN in a select set of domains is presented. The emphasis of discussion in each domain is on BN representation, drawing inferences and learning mechanism. In Section VIII, future research directions are provided for researchers interested in this domain. Finally, the paper is concluded in Section IX.