Corpus ID: 14155822

Bayesian methods in risk assessment

  title={Bayesian methods in risk assessment},
  author={Dominique Guyonnet},
Empirical data are almost always lacking in real-world risk analyses. In fact, some risk analysis problems try to forecast what risks may be associated with situations that are, at the time of the assessment, only hypothetical. It may therefore be impractical, unethical, or even impossible to collect relevant empirical data. To make matters worse for the analyst, the situations of concern in risk analyses are often novel and have never been studied before. This means that scientific… Expand
Coping with Nasty Surprises: Improving Risk Management in the Public Sector Using Simplified Bayesian Methods
The article concludes by showing how graphical plots of the incidence of true positives relative to false positives in test results can be used to assess diagnostic capabilities in an organisation—and also inform strategies for capability improvement. Expand
A systematic review of methods of uncertainty analysis and their applications in the assessment of chemical exposures, effects, and risks
  • L. Maxim
  • Engineering, Medicine
  • International journal of environmental health research
  • 2015
Whether current methods of uncertainty analysis are robust enough for regulatory use, because the methods used to protect public health must meet the most stringent scientific standards is determined. Expand
Empirical comparison of two methods for the Bayesian update of the parameters of probability distributions in a two-level hybrid probabilistic-possibilistic uncertainty framework for risk assessment
In this paper, we address the issue of updating in a Bayesian framework, the possibilistic representation of the epistemically-uncertain parameters of (aleatory) probability distributions, as newExpand
Combining imprecise Bayesian and maximum likelihood estimation for reliability growth models
A new framework is explored for combining imprecise Bayesian methods with likelihood inference, and it is presented in the context of reliability growth models. The main idea of the framework is toExpand
Assessment of Human Factor Performance Using Bayesian Inference and Inherent Safety
Simply attributing incidents to human error is not adequate; human factors aspects should be investigated such that lessons are learnt and the true root causes are established in order to preventExpand
Graphical Modelling in Mental Health Risk Assessments
Probabilistic models can be a combination of graph and probability theory that provide numerous advantages when it comes to the representation of domains involving uncertainty. In this paper, weExpand
Hierarchical Bayesian model for failure analysis of offshore wells during decommissioning and abandonment processes
The integration of Hierarchical Bayesian model with a Bayesian network to conduct the risk analysis of well decommissioning and abandonment processes is presented and the results demonstrate the potential of the proposed approach as a robust means to study complex well decommissionsing activities. Expand
Adaptive Management, Population Modeling and Uncertainty Analysis for Assessing the Impacts of Noise on Cetacean Populations
Population modeling is now widely used in threatened species management and for predicting the impacts and benefits of competing management options. However, some argue that the results of modelsExpand
Representing parametric probabilistic models tainted with imprecision
The limitations and disadvantages of the two-dimensional Monte-Carlo simulation approach are presented and a fuzzy random variable approach is proposed to treat this kind of knowledge. Expand
Recommendations to address uncertainties in environmental risk assessment using toxicokinetics-toxicodynamics models
The use of a Bayesian framework is investigated to obtain the uncertainties from the calibration process and to propagate them to model predictions, including LC(x, t) and MF( x, t), which could lead to robust derivation of toxicity endpoints leading to reliable EQSs. Expand


What Monte Carlo methods cannot do
Abstract Although extremely flexible and obviously useful for many risk assessment problems, Monte Carlo methods have four significant limitations that risk analysts should keep in mind. (1) LikeExpand
Propagation of uncertainty in risk assessments: the need to distinguish between uncertainty due to lack of knowledge and uncertainty due to variability.
The quantification of uncertainty for the simulation of a true but unknown distribution of values represents the state-of-the-art in assessment modeling. Expand
Assessment and Propagation of Model Uncertainty
A Bayesian approach to solving this problem that has long been available in principle but is only now becoming routinely feasible, by virtue of recent computational advances, is discussed and its implementation is examined in examples that involve forecasting the price of oil and estimating the chance of catastrophic failure of the U.S. Space Shuttle. Expand
Different methods are needed to propagate ignorance and variability
There are two kinds of uncertainty. One kind arises as variability resulting from heterogeneity or stochasticity. The other arises as partial ignorance resulting from systematic measurement error orExpand
Treatment of Uncertainty in Performance Assessments for Complex Systems
When viewed at a high level, performance assessments (PAs) for complex systems involve two types of uncertainty: stochastic uncertainty, which arises because the system under study can behave in manyExpand
Inferences from Multinomial Data: Learning About a Bag of Marbles
A new method is proposed for making inferences from multinomial data in cases where there is no prior information. A paradigm is the problem of predicting the colour of the next marble to be drawnExpand
Robust Bayesian credible intervals and prior ignorance
Summary In this paper we propose, survey and compare some classes of probability densities that may be used to represent partial prior information, to model either prior ignorance or BayesianExpand
Two perspectives on consensus for (Bayesian) inference and decisions
It is established that the only coherent preference schemes are the two agents' preferences themselves, with coherence defined by axiomatic restrictions on preferences over such acts. Expand
Sensitivity in Bayesian Statistics: The Prior and the Likelihood
Abstract One paradigm for sensitivity analyses in Bayesian statistics is to specify Γ, a reasonable class of priors, and to compute the corresponding class of posterior inferences. The class Γ isExpand
Are humans good intuitive statisticians after all? Rethinking some conclusions from the literature on judgment under uncertainty
Abstract Professional probabilists have long argued over what probability means, with, for example, Bayesians arguing that probabilities refer to subjective degrees of confidence and frequentistsExpand