141 research outputs found
An In-Depth Perspective on the Classical Model
No full textThe Classical Model (CM) or Cooke’s method for performing Structured Expert Judgement (SEJ) is the best-known method that promotes expert performance evaluation when aggregating experts’ assessments of uncertain quantities. Assessing experts’ performance in quantifying uncertainty involves two scores in CM, the calibration score (or statistical accuracy) and the information score. The two scores combine into overall scores, which, in turn, yield weights for a performance-based aggregation of experts’ opinions. The method is fairly demanding, and therefore carrying out a SEJ elicitation with CM requires careful consideration. This chapter aims to address the methodological and practical aspects of CM into a comprehensive overview of the CM elicitation process. It complements the chapter “Elicitation in the Classical Model” in the book Elicitation (Quigley et al. 2018). Nonetheless, we regard this chapter as a stand-alone material, hence some concepts and definitions will be repeated, for the sake of completeness.Applied Probabilit
Modelling risk in high hazard operations: Integrating technical, organisational and cultural factors
No full textRecent disasters in high hazard industries such as Oil and Gas Exploration (The Deepwater Horizon) and Petrochemical production (Texas City) have been found to have causes that range from direct technical failures through organizational shortcomings right up to weak regulation and inappropriate company cultures. Risk models have generally concentrated upon technical failures, which are easier to construct and for which there is more concrete data. The primary causes, as identified by the US Chemical Safety Board for Texas City and the Presidential Commission for the Deepwater Horizon, lie firmly rooted in the culture of the organization and determine the way in which individuals go about risky activities. Modelling human activities, especially collectively rather than individual human errors as is done in most human models, is a quite different proposition, in which complex interactions between different individuals and levels change over time as success and failure alter the pattern of payoffs. This paper examines the development of an integrated model for risk in a real-time environment for the hydrocarbon industry. It is based originally on the CATS model for commercial aviation safety, that first attempted to address some of these problems in a relatively simple way. Aviation is, however, a relatively simple activity, with large numbers of common components in a constrained environment. The Oil and Gas industry is significantly more diverse, covering the gamut from exploration, drilling, production, transport, refining and chemical production, each with its own potential for large scale disaster, but in the case of an integrated oil company all run by individuals within a common company culture. Other papers will cover the details of specific issues; this paper covers the integration of the model as a whole.Values and TechnologyTechnology, Policy and Managemen
Continuous/discrete non parametric Bayesian belief nets with UNICORN and UNINET
No full textHanea et al. (2006) presented a method for quantifying and computing continuous/discrete non parametric Bayesian Belief Nets (BBN). Influences are represented as conditional rank correlations, and the joint normal copula enables rapid sampling and conditionalization. Further mathematical background is in Kurowicka and Cooke (2007). This article sketches the current stage of development. The driving application currently involves 133 continuous and discrete probabilistic nodes, and 330 functional nodes. Boolean functions enable fault trees to be fully represented as functional nodes in a BBN. Repeated nodes are easily handled with the identity function. Current perspectives and challenges conclude the paper.Delft Institute of Applied MathematicsElectrical Engineering, Mathematics and Computer Scienc
Non-Parametric Bayesian Networks (NPBNs) versus Ensemble Kalman Filter (EnKF) in Reservoir Simulation with non-Gaussian Measurement Noise
No full textLately, the objective of reservoir engineering is to optimize hydrocarbon recovery from a reservoir. To achieve that goal, a good knowledge of the subsurface properties is crucial. The author is concerned with estimating one of the properties of the field: the permeability of a reservoir. To characterize the fluid flow, a two phase (oil-water) 2D model represented as a system of coupled nonlinear partial differential equations which is unsolvable analytically is used. Ensemble Kalman Filter (EnKF) is the most common tool used to deal with this situation. However, it is not the only way. Recently, a research on a more general approach based on a dynamic Bayesian network using the Non-Parametric Bayesian Networks (NPBNs) has been initiated. This research, which uses twin experiment, indicates that the NPBN approach appears to be a promising alternative to EnKF. However, a number of open questions emerge from this initial research. The first one is the normality assumption for the noise used in the measurements generation in the twin experiment. Even though Gaussian noise for measurements is sensible in the sense that the knowledge about the noise is unavailable, it does not mean that other noise from different distributions cannot be applied. The second one is the exclusion of saturation in the NPBN approach performed in the previous research. This may result in the loss of valuable information. Further, the previous research discovers that NPBN approach seems to work well in recovering only part of the reservoir. The entire permeability field may be approximated by means of interpolation between several approximated parts of the field. Hence, the third question relates to an interpolation method that may be used in recovering the permeability of the entire reservoir. This project aims to experiment on these three key points of interest. A fourth objective, however, is surfaced during the analysis, which is to use an alternative measure of performance to the well-known Root Mean Square Error (RMSE). Along the way, the performance of both EnKF and NPBN are going to be observed and compared one more time.Applied mathematicsElectrical Engineering, Mathematics and Computer Scienc
Editorial: Multivariate Probabilistic Modelling for Risk and Decision Analysis
No full textApplied Probabilit
Multivariate Methods for Coastal and Offshore Risks
No full textThis thesis investigates how selected multivariate probabilistic methods can be adapted for risk analysis and decision making in coastal and offshore environments. In particular, the thesis makes a contribution to decision support tools for risk reduction efforts in coastal environments and to statistical simulation methods for wave conditions. Generally, very few observations on negative impacts in coastal or offshore environments are available for risk analysis or decision making due to the rare nature of extreme events. However, synthetic impact data can be generated by propagating relevant hydro meteorological conditions to the environment of interest through a chain of multiple models. Especially in coastal environments, this chain often includes computationally intensive models.Coastal Engineerin
Levee system reliability modeling: The length effect and Bayesian updating
No full textIn levee system reliability, the length effect is the term given to the phenomenon that the longer the levee, the higher the probability that it will have a weak spot and fail. Quantitatively, it is the ratio of the segment failure probability to the cross-sectional failure probability. The literature is lacking in methods to calculate the length effect in levees, and often over-simplified methods are used. An efficient (but approximate) method, which we refer to as the modified outcrossing (MO) method, was developed for the system reliability model used in Dutch national flood risk analysis and for the provision of levee assessment tools, but it is poorly documented and its accuracy has not been tested. In this paper, we propose a method to calculate the length effect in levees by sampling the joint spatial distribution of the resistance variables using a copula approach, and represented by a Bayesian Network (BN). We use the BN to verify the MO method, which is also described in detail in this paper. We describe how both methods can be used to update failure probabilities of (long) levees using survival observations (i.e., high water levels and no levee failure), which is important because we have such observations in abundance. We compared the methods via a numerical example, and found that the agreement between the segment failure probability estimates was nearly perfect in the prior case, and very good in the posterior case, for segments ranging from 500 m to 6000 m in length. These results provide a strong verification of both methods, either of which provide an attractive alternative to the more simplified approaches often encountered in the literature and in practice.Hydraulic Structures and Flood RiskDynamics of Structure
Recent Advances in the Elicitation of Uncertainty Distributions from Experts for Multinomial Probabilities
No full textIn this chapter, we consider the problem of the elicitation and specification of an uncertainty distribution based on expert judgements, which may be a subjective prior distribution in a Bayesian analysis, for a set of probabilities which are constrained to sum to one. A typical context for this is as a prior distribution for the probabilities in a multinomial model. The Dirichlet distribution has long been advocated as a natural way to represent the uncertainty distribution over the probabilities in this context. The relatively small number of parameters allows for specification based on relatively few elicited quantities but at the expense of a very restrictive structure. We detail recent advances in elicitation for the Dirichlet distribution and recently proposed alternative approaches, which offer greater flexibility at the expense of added complexity. In order of increasing flexibility, they are the generalised Dirichlet distribution, multivariate copulas and vines. An extension of multinomial models containing covariates is discussed
Structured expert judgement for decisions on medicines policy and management
No full textMany decisions related to the marketing authorisation of medicinal products as well as decisions for processes such as Health Technology Assessment (HTA), reimbursement and pricing of medicines, and the setting of clinical guidelines, are taken in the face of significant uncertainties. Moreover, decision making can be impacted by biases resulting from psychological heuristics. In other domains where decisions have to be taken with imperfect or incomplete evidence, Structured Expert Judgement (SEJ) has been found to be useful in making the best use of available evidence, and synthesising it with professional expertise, stakeholders’ values and concerns. To date, formal SEJ has only been used to a limited extent in healthcare. Aspects affecting decisions for marketing authorisation and health technology assessment, reimbursement and pricing of medicines are described and the main risks and uncertainties are identified. Some considerations and recommendations for the use of SEJ to strengthen these decisions are made
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