Rock sampling

An apparatus for sampling rock and other brittle materials and for controlling resultant particle sizes is described. The device includes grinding means for cutting grooves in the rock surface and to provide a grouping of thin, shallow, parallel ridges and cutter means to reduce these ridges to a powder specimen. Collection means is provided for the powder. The invention relates to rock grinding and particularly to the sampling of rock specimens with good size control

Soil Sampling

This publication gives step-by-step instructions for sampling soil on your property. It gives the why, where and how of sampling, along with information necessary for having a sample analyzed.For more information, contact your local Cooperative Extension Service office or Jeff Smeenk at 907-746-9470 or [email protected]. This publication is a major revision by Jeff Smeenk of Soil Sampling, written by Wayne Vandre in April 1987. Reviewed by Jodie Anderson, Instructor, High Latitude Agriculture, School of Natural Resources and Agricultural Sciences, University of Alaska Fairbanks; Stephen Brown, Extension Faculty, Agriculture and Horticulture; and Gary Michaelson, Research Associate, Agricultural and Forestry Experiment Station, University of Alaska Fairbanks

Optimal random sampling designs in random field sampling

A Horvitz-Thompson predictor is proposed for spatial sampling when the characteristic of interest is modeled as a random field. Optimal sampling designs are deduced under this context. Fixed and variable sample size are considered

Diffusive Nested Sampling

We introduce a general Monte Carlo method based on Nested Sampling (NS), for sampling complex probability distributions and estimating the normalising constant. The method uses one or more particles, which explore a mixture of nested probability distributions, each successive distribution occupying ~e^-1 times the enclosed prior mass of the previous distribution. While NS technically requires independent generation of particles, Markov Chain Monte Carlo (MCMC) exploration fits naturally into this technique. We illustrate the new method on a test problem and find that it can achieve four times the accuracy of classic MCMC-based Nested Sampling, for the same computational effort; equivalent to a factor of 16 speedup. An additional benefit is that more samples and a more accurate evidence value can be obtained simply by continuing the run for longer, as in standard MCMC.Comment: Accepted for publication in Statistics and Computing. C++ code available at http://lindor.physics.ucsb.edu/DNes