Groundwater head sampling based on stochastic analysis

Spatial Sampling Design Based on Stochastic Complexity

A new methodology is introduced for spatial sampling design when the variable of interest cannot be directly observed, but information on it can be obtained by sampling a related variable, and estimation of the underlying model is required. An approach based on entropy has been proposed by Bueso, Angulo, and Alonso (1998, Environ. Ecol. Statist. 5, No. 1, 29 44) in the case where a model for th...

One-Week Module on Stochastic Groundwater Modeling

INTRODUCTION Students of groundwater hydrology enjoy several benefits from an introduction to groundwater modeling software, such as MODFLOW (Harbaugh and McDonald, 1996). From a practical perspective, students gain a skill that is useful in consulting practice. From an educational perspective, students gain insight into the geometry and dynamics of groundwater flow, since results are presented...

A Stochastic Optimization Model for Groundwater Management

On Sampling-controlled Stochastic Approximation1

In the general area of optimization under uncertainty, there are a large number of applications which require nding thèbest' values for a set of control variables or parameters and for which the only data available consist of measurements prone to random errors. Stochastic approximation provides a method of handling such noise or randomness in data; it has been widely studied in the literature ...

Stochastic image denoising based on Markov-chain Monte Carlo sampling

A novel stochastic approach based on Markov-Chain Monte Carlo sampling is investigated for the purpose of image denoising. The additive image denoising problem is formulated as a Bayesian least squares problem, where the goal is to estimate the denoised image given the noisy image as the measurement and an estimated posterior. The posterior is estimated using a nonparametric importance-weighted...