Advanced & Applied Topics · Spatial Statistics & Geostatistics

Spatial Statistics: Bayesian Approach and Advanced Computational Methods

Public introduction · full guide in Eikos

Introduction

What this guide covers

Introduction Introduction Computational Bayesian Statistics · Doctorate Level

Overview

Topics covered

  • Introduction
  • Bayesian Spatial Statistics and Advanced Computational Methods
  • Introduction and Fundamentals of Spatial Statistics
  • Nature and typology of spatial data
  • Tobler's First Law and the Neighborhood Structure
  • Geostatistics and Gaussian Processes (Continuous Models)

Related concepts

Main connections

  • Bayes' theorem & priors
  • Linear algebra & matrices
  • Markov chain Monte Carlo (MCMC)
  • Variance & standard deviation
  • Calculus & differentiation
  • Poisson distribution

Compatible notation

Mathematical sample in LaTeX

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

wij={1if SiAndSjThey share a common border(i∼j)0In another case (with conventionwii=0)
View LaTeX sourcew_{ij} = \begin{cases} 1 & \text{if } S_i \text{And} S_j \text{They share a common border} (i \sim j) \\ 0 & \text{In another case (with convention} w_{ii} = 0) \end{cases}