Editing Geostatistics (section)

===Estimation===

==== Kriging ====
{{Main|Kriging}}
Kriging is a group of geostatistical techniques to interpolate the value of a random field (e.g., the elevation, z, of the landscape as a function of the geographic location) at an unobserved location from observations of its value at nearby locations.

==== Bayesian estimation ====
{{Main|Bayesian inference}}
Bayesian inference is a method of statistical inference in which [[Bayes' theorem]] is used to update a probability model as more evidence or information becomes available. Bayesian inference is playing an increasingly important role in geostatistics.<ref>Banerjee S., Carlin B.P., and Gelfand A.E. (2014). Hierarchical Modeling and Analysis for Spatial Data, Second Edition. Chapman & Hall/CRC Monographs on Statistics & Applied Probability. {{ISBN|9781439819173}}</ref> Bayesian estimation implements kriging through a spatial process, most commonly a [[Gaussian process]], and updates the process using [[Bayes' Theorem]] to calculate its posterior. High-dimensional Bayesian geostatistics.<ref>Banerjee, Sudipto. High-Dimensional Bayesian Geostatistics. Bayesian Anal. 12 (2017), no. 2, 583--614. {{doi|10.1214/17-BA1056R}}. https://projecteuclid.org/euclid.ba/1494921642</ref>

==== Finite difference method ====
Considering the principle of conservation of probability, recurrent difference equations (finite difference equations) were used in conjunction with lattices to compute probabilities quantifying uncertainty about the geological structures. This procedure is a numerical alternative method to Markov chains and Bayesian models.<ref>{{cite journal|last1= Cardenas |first1=IC|title= A two-dimensional approach to quantify stratigraphic uncertainty from borehole data using non-homogeneous random fields|journal=Engineering Geology|date=2023|doi=10.1016/j.enggeo.2023.107001|doi-access=free}}</ref>