Influence Diagrams

Howard, Ronald A.; Matheson, James

doi:10.1287/deca.1050.0020

Cited by 554 publications

(428 citation statements)

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“…Several researchers have developed probabilistic inference algorithms for Bayesian networks with discrete variables that exploit conditional independence roughly as we h a ve described, although with di erent t wists. For example, Howard and Matheson 1981, Olmsted 1983, and Shachter 1988 developed an algorithm that reverses arcs in the network structure until the answer to the given probabilistic query can be read directly from the graph. In this algorithm, each arc reversal corresponds to an application of Bayes' theorem.…”

Section: Inference In a Bayesian Networkmentioning

confidence: 99%

A Tutorial on Learning with Bayesian Networks

Heckerman

Studies in Computational Intelligence

986

961

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A companion set of lecture slides is available at ftp: ftp.research.microsoft.com pub dtg david tutorial.ps. AbstractA B a yesian network is a graphical model that encodes probabilistic relationships among variables of interest. When used in conjunction with statistical techniques, the graphical model has several advantages for data analysis. One, because the model encodes dependencies among all variables, it readily handles situations where some data entries are missing. Two, a Bayesian network can be used to learn causal relationships, and hence can be used to gain understanding about a problem domain and to predict the consequences of intervention. Three, because the model has both a causal and probabilistic semantics, it is an ideal representation for combining prior knowledge which often comes in causal form and data. Four, Bayesian statistical methods in conjunction with Bayesian networks o er an e cient and principled approach for avoiding the over tting of data. In this paper, we discuss methods for constructing Bayesian networks from prior knowledge and summarize Bayesian statistical methods for using data to improve these models. With regard to the latter task, we describe methods for learning both the parameters and structure of a Bayesian network, including techniques for learning with incomplete data. In addition, we relate Bayesian-network methods for learning to techniques for supervised and unsupervised learning. We illustrate the graphical-modeling approach using a real-world case study.

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Section: Inference In a Bayesian Networkmentioning

confidence: 99%

A Tutorial on Learning with Bayesian Networks

Heckerman

Studies in Computational Intelligence

986

961

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“…If not, then a variable is said to fail the clarity test [31]. This will often produce problems at later steps in the model development process.…”

Section: B Designmentioning

confidence: 99%

A methodology for developing local smart diagnostic models using expert knowledge

Madsen

Søndberg-Jeppesen

Lohse

et al. 2015

2015 IEEE 13th International Conference on Industrial Informatics (INDIN)

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Abstract-This paper describes an innovative modular component-based modelling approach for diagnostics and condition-monitoring of manufacturing equipment. The approach is based on the use of object-oriented Bayesian networks, which supports a natural decomposition of a large and complex system into a set of less complex components. The methodology consists of six steps supporting the development process: Begin, Design, Implement, Test, Analyse, and Deploy. The process is iterative and the steps should be repeated until a satisfactory model has been achieved. The paper describes the details of the methodology as well as illustrates the use of the componentbased modelling approach on a linear axis used in manufacturing. This application demonstrates the power and flexibility of the approach for diagnostics and condition-monitoring and shows a significant potential of the approach for modular componentbased modelling in manufacturing and other domains.

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“…These techniques include fault trees [12], Markov models [9,13], and Bayesian networks [2]. Influence diagrams [10] were originally a graphical language designed to support decision making by specifying the factors influencing a decision. In [5], such diagrams are connected to the leaf nodes of fault trees supporting the propagation of influence to the unwanted incidents specified at the root of the tree.…”

Section: Related Workmentioning

confidence: 99%

Security risk analysis of system changes exemplified within the oil and gas domain

Refsdal

Solhaug

Stølen

2014

Int J Softw Tools Technol Transfer

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Changes, such as the introduction of new technology, may have considerable impact on the risk to which a system or organization is exposed. For example, in the oil & gas domain, introduction of technology that allows offshore installations to be operated from onshore means that fewer people are exposed to risk on the installation, but it also introduces new risks and vulnerabilities. We need suitable methods and techniques in order to understand how a change will affect the risk picture. This paper presents an approach that offers specialized support for analysis of risk with respect to change. The approach allows links between elements of the target of analyses and the related parts of the risk model to be explicitly captured, which facilitates tool support for identifying the parts of a risk model that need to be reconsidered when a change is made to the target. Moreover, the approach offers language constructs for capturing the risk picture before and after a change. The approach is demonstrated on a case concerning new software technology to support decision making on petroleum installations.

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Influence Diagrams

Cited by 554 publications

References 1 publication

A Tutorial on Learning with Bayesian Networks

A Tutorial on Learning with Bayesian Networks

A methodology for developing local smart diagnostic models using expert knowledge

Security risk analysis of system changes exemplified within the oil and gas domain

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