Roger M. Cooke

Analysis of 1.9 Mb of contiguous sequence from chromosome 4 of Arabidopsis thaliana

The plant Arabidopsis thaliana (Arabidopsis) has become an important model species for the study of many aspects of plant biology. The relatively small size of the nuclear genome and the availability of extensive physical maps of the five chromosomes provide a feasible basis for initiating sequencing of the five chromosomes. The YAC (yeast artificial chromosome)-based physical map of chromosome 4 was used to construct a sequence-ready map of cosmid and BAC (bacterial artificial chromosome) clones covering a 1.9-megabase (Mb) contiguous region, and the sequence of this region is reported here. Analysis of the sequence revealed an average gene density of one gene every 4.8 kilobases (kb), and 54% of the predicted genes had significant similarity to known genes. Other interesting features were found, such as the sequence of a disease-resistance gene locus, the distribution of retroelements, the frequent occurrence of clustered gene families, and the sequence of several classes of genes not previously encountered in plants.

Probability Density Decomposition for Conditionally Dependent Random Variables Modeled by Vines

A vine is a new graphical model for dependent random variables. Vines generalize the Markov trees often used in modeling multivariate distributions. They differ from Markov trees and Bayesian belief nets in that the concept of conditional independence is weakened to allow for various forms of conditional dependence. A general formula for the density of a vine dependent distribution is derived. This generalizes the well-known density formula for belief nets based on the decomposition of belief nets into cliques. Furthermore, the formula allows a simple proof of the Information Decomposition Theorem for a regular vine. The problem of (conditional) sampling is discussed, and Gibbs sampling is proposed to carry out sampling from conditional vine dependent distributions. The so-called ‘canonical vines’ built on highest degree trees offer the most efficient structure for Gibbs sampling.

TU Delft expert judgment data base

We review the applications of structured expert judgment uncertainty quantification using the “classical model” developed at the Delft University of Technology over the last 17 years [Cooke RM. Experts in uncertainty. Oxford: Oxford University Press; 1991; Expert judgment study on atmospheric dispersion and deposition. Report Faculty of Technical Mathematics and Informatics No.01-81, Delft University of Technology; 1991]. These involve 45 expert panels, performed under contract with problem owners who reviewed and approved the results. With a few exceptions, all these applications involved the use of seed variables; that is, variables from the experts’ area of expertise for which the true values are available post hoc. Seed variables are used to (1) measure expert performance, (2) enable performance-based weighted combination of experts’ distributions, and (3) evaluate and hopefully validate the resulting combination or “decision maker”. This article reviews the classical model for structured expert judgment and the performance measures, reviews applications, comparing performance-based decision makers with “equal weight” decision makers, and collects some lessons learned.

Expert judgement elicitation for risk assessments of critical infrastructures

Governmental bodies and companies are confronted with the problem of achieving rational consensus in the face of substantial uncertainties. The subject area of this special issue (risk and vulnerability assessments and management of critical infrastructures) might be a good example as are risk management of chemical installations and accident consequence management for nuclear power plants. Decisions with regard to infrastructures functioning and possible malfunctioning must be taken on the basis of predictions of technical and organizational system behaviour. These predictions use mathematical models containing scores of uncertain parameters. Decision makers want to take, and want to be perceived to take, these decisions in a rational manner. The question is, how can this be accomplished in the face of large uncertainties? One available source is experts in the many fields of interest within infrastructures. This paper describes the use of structured expert judgement in a formal manner. The paper refers to the Procedures Guide published by the European Union as EUR 18820. This Procedures Guide addresses two methods for using expert judgements developed at Delft University of Technology. The paired comparisons method is particularly suitable to identify the relative importance of attributes in the risk management arena, while the Classical Model, apt to arrive at subjective probability assessments, is particularly suitable to derive uncertainty distributions over model parameters. Examples will be referred to for further illustration of applications relevant in the field of risk assessment and risk management.

Using expert judgment to estimate marine ecosystem vulnerability in the California Current.

As resource management and conservation efforts move toward multi-sector, ecosystem-based approaches, we need methods for comparing the varying responses of ecosystems to the impacts of human activities in order to prioritize management efforts, allocate limited resources, and understand cumulative effects. Given the number and variety of human activities affecting ecosystems, relatively few empirical studies are adequately comprehensive to inform these decisions. Consequently, management often turns to expert judgment for information. Drawing on methods from decision science, we offer a method for eliciting expert judgment to (1) quantitatively estimate the relative vulnerability of ecosystems to stressors, (2) help prioritize the management of stressors across multiple ecosystems, (3) evaluate how experts give weight to different criteria to characterize vulnerability of ecosystems to anthropogenic stressors, and (4) identify key knowledge gaps. We applied this method to the California Current region in order to evaluate the relative vulnerability of 19 marine ecosystems to 53 stressors associated with human activities, based on surveys from 107 experts. When judging the relative vulnerability of ecosystems to stressors, we found that experts primarily considered two criteria: the ecosystems resistance to the stressor and the number of species or trophic levels affected. Four intertidal ecosystems (mudflat, beach, salt marsh, and rocky intertidal) were judged most vulnerable to the suite of human activities evaluated here. The highest vulnerability rankings for coastal ecosystems were invasive species, ocean acidification, sea temperature change, sea level rise, and habitat alteration from coastal engineering, while offshore ecosystems were assessed to be most vulnerable to ocean acidification, demersal destructive fishing, and shipwrecks. These results provide a quantitative, transparent, and repeatable assessment of relative vulnerability across ecosystems to any ongoing or emerging human activity. Combining these results with data on the spatial distribution and intensity of human activities provides a systematic foundation for ecosystem-based management.

Expert judgment in maintenance optimization

A comprehensive method for the use of expert opinion for obtaining lifetime distributions required for maintenance optimization is proposed. The method includes procedures for the elicitation of discretized lifetime distributions from several experts, the combination of the elicited expert opinion into a consensus distribution, and the updating of the consensus distribution with failure and maintenance data. The development of the method was prompted by the lack of statistical training of the experts and the high demands on their time. The use of a discretized life distribution provides more flexibility, is more comprehendible by the experts in the elicitation stage, and greatly reduces the computation in the combination and updating stages. The methodology is Bayes, using the Dirichlet distribution as the prior distribution for the elicited discrete lifetime distribution. Methods are described for incorporating information concerning the expertise of the experts into the analysis. >

Prioritizing emerging zoonoses in The Netherlands.

Background To support the development of early warning and surveillance systems of emerging zoonoses, we present a general method to prioritize pathogens using a quantitative, stochastic multi-criteria model, parameterized for the Netherlands. Methodology/Principal Findings A risk score was based on seven criteria, reflecting assessments of the epidemiology and impact of these pathogens on society. Criteria were weighed, based on the preferences of a panel of judges with a background in infectious disease control. Conclusions/Significance Pathogens with the highest risk for the Netherlands included pathogens in the livestock reservoir with a high actual human disease burden (e.g. Campylobacter spp., Toxoplasma gondii, Coxiella burnetii) or a low current but higher historic burden (e.g. Mycobacterium bovis), rare zoonotic pathogens in domestic animals with severe disease manifestations in humans (e.g. BSE prion, Capnocytophaga canimorsus) as well as arthropod-borne and wildlife associated pathogens which may pose a severe risk in future (e.g. Japanese encephalitis virus and West-Nile virus). These agents are key targets for development of early warning and surveillance.

Attribution of Foodborne Pathogens Using Structured Expert Elicitation

OBJECTIVES To estimate the fraction of human cases of enterically transmitted illness by five major pathways (food, environment, direct animal contact, human-human transmission, and travel) and by 11 groups within the food pathway. METHODS Food safety experts were asked to provide their estimates of the most likely range for each of the parameters. Joint probability distributions were created by probabilistic inversion (PI). RESULTS Sixteen experts participated in the study. PI resulted in good fits for most pathogens. Qualitatively, expert estimates were similar to earlier published studies but the estimated fraction of foodborne transmission was lower for most pathogens. Biologically less plausible pathways were given some weight by the experts. Uncertainties were smallest for pathogens with dominant transmission routes. CONCLUSIONS Structured expert studies are a feasible method for source attribution, but methods need further development. APPLICATIONS These estimates can be combined with data on incidence, disease burden and costs to provide specific estimates of the public health impact of foodborne illness, and to identify the food groups that have the highest impact.

Hybrid Method for Quantifying and Analyzing Bayesian Belief Nets

Bayesian belief nets (BBNs) have become a popular tool for specifying high-dimensional probabilistic models. Commercial tools with an advanced graphical user interface that support BBNs construction and inference are available. Thus, building and working with BBNs is very efficient as long as one is not forced to quantify complex BBNs. A high assessment burden of discrete BBNs is often caused by the discretization of continuous variables. Until recently, continuous BBNs were restricted to the joint normal distribution. We present the ‘copula–vine’ approach to continuous BBNs. This approach is quite general and allows traceable and defendable quantification methods, but it comes at a price: these BBNs must be evaluated by Monte Carlo simulation. Updating such a BBN requires re-sampling the whole structure. The advantages of fast updating algorithms for discrete BBNs are decisive. A hybrid method advanced here samples the continuous BBN once, and then discretizes this so as to enable fast updating. This combines the reduced assessment burden and modelling flexibility of the continuous BBNs with the fast updating algorithms of discrete BBNs. Sampling large complex structures only once can still involve time consuming numerical calculations. Therefore a new sampling protocol based on normal vines is developed. Normal vines are used to realize the dependence structure specified via (conditional) rank correlations on the continuous BBN. We will emphasize the advantages of this method by means of examples. Copyright

Further development of a Causal model for Air Transport Safety (CATS): Building the mathematical heart

The development of the Netherlands international airport Schiphol has been the subject of fierce political debate for several decades. One of the considerations has been the safety of the population living around the airport, the density of which has been and still is growing. In the debate about the acceptability of the risks associated with the air traffic above, The Netherlands extensive use has been made of statistical models relating the movement of airplanes to the risks on the ground. Although these models are adequate for the debate and for physical planning around the airport, the need has arisen to gain a more thorough understanding of the accident genesis in air traffic, with the ultimate aim of improving the safety situation in air traffic in general and around Schiphol in particular. To this aim, a research effort has started to develop causal models for air traffic risks in the expectation that these will ultimately give the insight needed. The concept was described in an earlier paper. In this paper, the backbone of the model and the way event sequence diagrams, fault-trees and Bayesian belief nets are linked to form a homogeneous mathematical model suitable as a tool to analyse causal chains and quantify risks is described.