A model for building statistical dependence between marginal distribution with bounded support is discussed. The model is geared towards elicitation of dependence parameters through expert judgment. The resulting joint distribution may be useful in uncertainty analyses where dependence between random variables with a bounded support is present due to common risk factors, such as, e.g., in the classical Project Evaluation and Review Technique. Author Keywords: Uncertainty modeling; Mixture of uniform distributions; Expert judgment@en

The Federal Emergency Management Agency¿s (FEMA¿s) path to Homeland Security was paved for 24 years by (1) the actions of five presidents and by new laws, (2) by the outcomes of the agency¿s bureaucratic ¿turf wars¿ with other federal departments or agencies, (3) by staffing decisions inside the agency that relied on ¿generalist¿ managers more than ¿technocratic¿ managers, and (4) experience with human-caused disasters ranging from terror bombings to radiological and hazardous materials incidents, under all-hazards emergency management. These are the determinants that prepared FEMA for a major role in homeland security.@en

Recent studies in the assessment of risk in maritime transportation systems have used simulation-based probabilistic techniques. Amongst them are the San Francisco Bay (SFB) Ferry exposure assessment in 2002, the Washington State Ferry (WFS) Risk Assessment in 1998 and the Prince William Sound (PWS) Risk Assessment in 1996. Representing uncertainty in such simulation models is fundamental to quantifying system risk. This paper illustrates the representation of uncertainty in simulation using Bayesian techniques to model input and output uncertainty. These uncertainty representations describe system randomness as well as lack of knowledge about the system. The study of the impact of proposed ferry service expansions in San Francisco Bay is used as a case study to demonstrate the Bayesian simulation technique. Such characterization of uncertainty in simulation-based analysis provides the user with a greater level of information enabling improved decision making.@en

This article presents the development of a general Bayes inference model for accelerated life testing. The failure times at a constant stress level are assumed to belong to a Weibull distribution, but the specification of strict adherence to a parametric time-transformation function is not required. Rather, prior information is used to indirectly define a multivariate prior distribution for the scale parameters at the various stress levels and the common shape parameter. Using the approach, Bayes point estimates as well as probability statements for use-stress (and accelerated) life parameters may be inferred from a host of testing scenarios. The inference procedure accommodates both the interval data sampling strategy and type I censored sampling strategy for the collection of ALT test data. The inference procedure uses the well-known MCMC (Markov Chain Monte Carlo) methods to derive posterior approximations. The approach is illustrated with an example. Keywords: Dirichlet distribution; Environmental testing; Step-stress testing@en