here in, an application of a new seismic inversion algorithm in one of iran’s oilfields is described. stochastic (geostatistical) seismic inversion, as a complementary method to deterministic inversion, is perceived as contribution combination of geostatistics and seismic inversion algorithm. this method integrates information from different data sources with different scales, as prior informat...

Here in, an application of a new seismic inversion algorithm in one of Iran’s oilfields is described. Stochastic (geostatistical) seismic inversion, as a complementary method to deterministic inversion, is perceived as contribution combination of geostatistics and seismic inversion algorithm. This method integrates information from different data sources with different scales, as prior informat...

We propose a prior probability model in the wavelet coeecient space. The proposed model implements wavelet coeecient thresholding by full posterior inference in a coherent probability model. We introduce a prior probability model with mixture priors for the wavelet coeecients. The prior includes a positive prior probability mass at zero which leads to a posteriori threshold-ing and generally to...

in order to perform preventive maintenance (pm), two approaches have evolved in the literature. the traditional approach is based on the use of statistical and reliability analysis of equipment failure. under statistical-reliability (s-r)-based pm, the objective of achieving the minimum total cost is pursued by establishing fixed pm intervals, which are statistically optimal, at which to replac...

The complete final product of Bayesian inference is the posterior distribution of the quantity of interest. Important inference summaries include point estimation, region estimation, and precise hypotheses testing. Those summaries may appropriately be described as the solution to specific decision problems which depend on the loss function chosen. The use of a continuous loss function leads to ...

Studies of learning algorithms typically concentrate on situations where potentially ever growing training sample is available. Yet, there can be situations (e.g., detection of differentially expressed genes on unreplicated data or estimation of time delay in non-stationary gravitationally lensed photon streams) where only extremely small samples can be used in order to perform an inference. On...