Bayesian Kriging & Simulations
type: Dataset
contact point: Organization
  fn: CCSD
keyword: drift
keyword: simulations
keyword: infoeu-reposemanticsreport
keyword: bayesian
keyword: statthstatistics-statstatistics-theory-statth
keyword: reports
keyword: computing
keyword: kriging
landing page: https://minesparis-psl.hal.science/hal-00957761
description: This paper gives a presentation of the version of Kriging (BK) and Simulations (SK) with Bayesian guess of the drift coe cients. In particular, it shows that BK stands between the Simple Kriging (SK) where the drift is assumed to be known and the universal Kriging (UK) where there is no prior information on the drift. This paper is heavily inspired by the course of Bayesian approach given by Ch. Lajaunie during the Automnales at the Centre de Géosciences of the Ecole des Mines de Paris in Fontainebleau. It also contains several references from a paper by Omre & Halvorsen [1].
issued: 2026-05-06T02:13:05.129240
language: en
modified: 2026-05-06T02:13:05.129244

test_moissonnage_selune
type: Agent

Bayesian Kriging & Simulations
type: Distribution
access URL: https://minesparis-psl.hal.science/hal-00957761
format: HTML
issued: 2026-05-06T02:13:05.159054
modified: 2026-05-06T02:13:05.113923