Eikosstatistics & data science

Advanced & Applied Topics · Spatial Statistics & Geostatistics

Spatial Statistics: Geostatistics — Advanced Theory and Predictive Inference

Public introduction · full guide in Eikos

Introduction

What this guide covers

Introduction Introduction Spatial Statistics — Process Theory

Overview

Topics covered

  • Introduction
  • Stochastic Geometry: Geostatistics
  • Historical Introduction and Context of the Discipline
  • Probabilistic Support: Random Field Theory
  • The Multivariate Spatial Distribution
  • Hypothesis of Stationarity in Spatial Processes

Related concepts

Main connections

  • Variance & standard deviation
  • Time series & ARIMA
  • Maximum likelihood estimation
  • Mean & expected value
  • Linear algebra & matrices
  • Mathematical expectation & moments

Compatible notation

Mathematical sample in LaTeX

The expression is rendered accessibly, with its LaTeX source preserved below.

F𝐬1,…,𝐬k(z1,…,zk)=P(Z(𝐬1)≤z1,Z(𝐬2)≤z2,…,Z(𝐬k)≤zk)
View LaTeX sourceF_{\mathbf{s}_1, \dots, \mathbf{s}_k}(z_1, \dots, z_k) = P\Big(Z(\mathbf{s}_1) \le z_1, Z(\mathbf{s}_2) \le z_2, \dots, Z(\mathbf{s}_k) \le z_k\Big)