https://hal.archives-ouvertes.fr/hal-00546144/document

Overview on Bayesian networks applications for dependability, risk analysis and maintenance areas

Bayesian network (BN) modeling is a rapidly advancing field. Here we explore new methods by which BN model development and application are being joined with other tools and model frameworks. Advances include improving areas of Bayesian classifiers and machine-learning algorithms for model structuring and parameterization, and development of time-dynamic models. Increasingly, BN models are being integrated with: management decision networks; structural equation modeling of causal networks; Bayesian neural networks; combined discrete and continuous variables; object-oriented and agent-based models; state-and-transition models; geographic information systems; quantum probability; and other fields. Integrated BNs (IBNs) are becoming useful tools in risk analysis, risk management, and decision science for resource planning and environmental management. In the near future, IBNs may become self-structuring, self-learning systems fed by real-time monitoring data. Such advances may make model validation difficult, and may question model credibility, particularly if based on uncertain sources of knowledge systems and big data.

https://www.fs.fed.us/pnw/pubs/journals/pnw_2019_marcot002.pdf

Advances in Bayesian network modelling: Integration of modelling technologies

Statistical pattern recognition relates to the use of statistical techniques for analysing data measurements in order to extract information and make justified decisions. It is a very active area of study and research, which has seen many advances in recent years. Applications such as data mining, web searching, multimedia data retrieval, face recognition, and cursive handwriting recognition, all require robust and efficient pattern recognition techniques.

Statistical Pattern Recognition: Webb/Statistical Pattern Recognition

Applications of Bayesian networks and Petri nets in safety, reliability, and risk assessments: A review

The Fourth Uncertainty in Artificial Intelligence workshop was held 19-21 August 1988. The workshop featured significant developments in application of theories of representation and reasoning under uncertainty. A recurring idea at the workshop was the need to examine uncertainty calculi in the context of choosing representation, inference, and control methodologies. The effectiveness of these choices in AI systems tends to be best considered in terms of specific problem areas. These areas include automated planning, temporal reasoning, computer vision, medical diagnosis, fault detection, text analysis, distributed systems, and behavior of nonlinear systems. Influence diagrams are emerging as a unifying representation, enabling tool development. Interest and results in uncertainty in AI are growing beyond the capacity of a workshop format.

/pdf/uncertainty-in-artificial-intelligence-5bwjag3cna.pdf

Uncertainty in artificial intelligence

We consider approximate inference in hybrid Bayesian Networks (BNs) and present a new iterative algorithm that efficiently combines dynamic discretization with robust propagation algorithms on junction trees. Our approach offers a significant extension to Bayesian Network theory and practice by offering a flexible way of modeling continuous nodes in BNs conditioned on complex configurations of evidence and intermixed with discrete nodes as both parents and children of continuous nodes. Our algorithm is implemented in a commercial Bayesian Network software package, AgenaRisk, which allows model construction and testing to be carried out easily. The results from the empirical trials clearly show how our software can deal effectively with different type of hybrid models containing elements of expert judgment as well as statistical inference. In particular, we show how the rapid convergence of the algorithm towards zones of high probability density, make robust inference analysis possible even in situations where, due to the lack of information in both prior and data, robust sampling becomes unfeasible.

Inference in hybrid Bayesian networks using dynamic discretization

This report describes the use of Bayesian networks (BNs) to model statistical loss distributions in financial operational risk scenarios. Its focus is on modeling “long” tail, or unexpected, loss events using mixtures of appropriate loss frequency and severity distributions where these mixtures are conditioned on causal variables that model the capability or effectiveness of the underlying controls process. The use of causal modeling is discussed from the perspective of exploiting local expertise about process reliability and formally connecting this knowledge to actual or hypothetical statistical phenomena resulting from the process. This brings the benefit of supplementing sparse data with expert judgment and transforming qualitative knowledge about the process into quantitative predictions. We conclude that BNs can help combine qualitative data from experts and quantitative data from historical loss databases in a principled way and as such they go some way in meeting the requirements of the draft Basel II Accord (Basel, 2004) for an advanced measurement approach (AMA).

/pdf/using-bayesian-networks-to-model-expected-and-unexpected-11yllq2fe9.pdf

Using Bayesian networks to model expected and unexpected operational losses.

A hybrid Bayesian network (BN) is one that incorporates both discrete and continuous nodes. In our extensive applications of BNs for system dependability assessment the models are invariably hybrid and the need for efficient and accurate computation is paramount. We apply a new iterative algorithm that efficiently combines dynamic discretisation with robust propagation algorithms on junction tree structures to perform inference in hybrid BNs. We illustrate its use on two example dependability problems: reliability estimation and diagnosis of a faulty sensor in a temporal system. Dynamic discretisation can be used as an alternative to analytical or Monte Carlo methods with high precision and can be applied to a wide range of dependability problems.

/pdf/modeling-dependable-systems-using-hybrid-bayesian-networks-38dblo3crn.pdf

Modeling dependable systems using hybrid Bayesian networks

Software metrics should support managerial decision making in software projects. We explain how traditional metrics approaches, such as regression-based models for cost estimation fall short of this goal. Instead, we describe a causal model (using a Bayesian network) which incorporates empirical data, but allows it to be interpreted and supplemented using expert judgement. We show how this causal model is used in a practical decision-support tool, allowing a project manager to trade-off the resources used against the outputs (delivered functionality, quality achieved) in a software project. The model and toolset have evolved in a number of collaborative projects and hence capture significant commercial input. Extensive validation trials are taking place among partners on the EC funded project MODIST (this includes Philips, Israel Aircraft Industries and QinetiQ) and the feedback so far has been very good. The estimates are sensible and the causal modelling approach enables decision-makers to reason in a way that is not possible with other project management and resource estimation tools. To ensure wide dissemination and validation a version of the toolset with the full underlying model is being made available for free to researchers.

/pdf/making-resource-decisions-for-software-projects-3ua4onzuks.pdf

Making resource decisions for software projects

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Inference in hybrid Bayesian networks using dynamic discretization

Using Bayesian networks to model expected and unexpected operational losses.

Modeling dependable systems using hybrid Bayesian networks

Making resource decisions for software projects