Treatment of input uncertainty in hydrologic modeling: Doing hydrology backward with Markov chain Monte Carlo simulation

Bayesian methods, and particularly Markov chain Monte Carlo (MCMC) techniques, are extremely useful in uncertainty assessment and parameter estimation of hydrologic models. However, MCMC algorithms can be difficult to implement successfully because…

Adequate security margins are normally applied in power systems by keeping a pre-defined set of transfer limits through certain transmission corridors in the system. This will protect the transmission lines from overheating causing a violation of…

This paper demonstrates for the first time the use of Markov Chain Monte Carlo (MCMC) simulation for parameter inference in model-based soil geostatistics. We implemented the recently developed DiffeRential Evolution Adaptive Metropolis (DREAM)…

The use of simulation for high-dimensional intractable computations has revolutionized applied mathematics. Designing, improving and understanding the new tools leads to (and leans on) fascinating mathematics, from representation theory through…

Monte Carlo simulation has become the accepted method for propagating parameter uncertainty through risk models. It is widely appreciated, however, that correlations between input variables must be taken into account if models are to deliver correct…

Multimedia environmental fate models are useful tools to investigate the long-term impacts of remediation measures designed to alleviate potential ecological and human health concerns in contaminated areas. Estimating and communicating the…

In this paper, we present a soft-input soft-output(SISO) detection scheme based on Markov Chain Monte Carlo(MCMC) simulation technique for Multiple-Input-Multiple-Output(MIMO) systems. The MCMC technique uses Metropolis-Hasting algorithm to obtain…

Abstract Adequate security margins are commonly applied in power systems by keeping predefined transfer limits through certain transmission corridors in the system. These limits are often set to keep the N- 1 criterion stating that the system should…

Computation of the marginal likelihood from a simulated posterior distribution is central to Bayesian model selection but is computationally difficult. I argue that the marginal likelihood can be reliably computed from a posterior sample by careful…

Field observed performance of slopes can be used to back calculate input parameters of soil properties and evaluate uncertainty of a slope stability analysis model. In this paper, a new probabilistic method is proposed for back analysis of slope…