Curve fitting | Multivariate interpolation | Interpolation
In statistics, originally in geostatistics, kriging or Kriging, also known as Gaussian process regression, is a method of interpolation based on Gaussian process governed by prior covariances. Under suitable assumptions of the prior, kriging gives the best linear unbiased prediction (BLUP) at unsampled locations. Interpolating methods based on other criteria such as smoothness (e.g., smoothing spline) may not yield the BLUP. The method is widely used in the domain of spatial analysis and computer experiments. The technique is also known as Wiener–Kolmogorov prediction, after Norbert Wiener and Andrey Kolmogorov. The theoretical basis for the method was developed by the French mathematician Georges Matheron in 1960, based on the master's thesis of Danie G. Krige, the pioneering plotter of distance-weighted average gold grades at the Witwatersrand reef complex in South Africa. Krige sought to estimate the most likely distribution of gold based on samples from a few boreholes. The English verb is to krige, and the most common noun is kriging; both are often pronounced with a hard "g", following an Anglicized pronunciation of the name "Krige". The word is sometimes capitalized as Kriging in the literature. Though computationally intensive in its basic formulation, kriging can be scaled to larger problems using various approximation methods. (Wikipedia).
Clustering 1: monothetic vs. polythetic
Full lecture: http://bit.ly/K-means The aim of clustering is to partition a population into sub-groups (clusters). Clusters can be monothetic (where all cluster members share some common property) or polythetic (where all cluster members are similar to each other in some sense).
From playlist K-means Clustering
MATLAB Basics: Get The Most Out of MATLAB
In this livestream, Heather Gorr and Elsie Eigerman will be walking through the fundamentals of programming with MATLAB. This isn’t just for beginners; we’ll show you the latest and greatest tips and tricks to help you get the most out of MATLAB. We’ll also walk-through core concepts for t
From playlist MATLAB and Simulink Livestreams
If you are interested in learning more about this topic, please visit http://www.gcflearnfree.org/ to view the entire tutorial on our website. It includes instructional text, informational graphics, examples, and even interactives for you to practice and apply what you've learned.
From playlist Machine Learning
We Need a Bigger Definition of Creativity
► Please Subscribe to My Channel Here - http://bit.ly/spencervideos When you the word “creative,” you might think of a painter or a playwright or an author or a photographer or a filmmaker or a chef. In other words, you might think of people who make things. I think it’s what we mean wh
From playlist What Is Creativity?
Introduction to the C programming language. Part of a larger series teaching programming. See http://codeschool.org
From playlist The C language
Introduction to the C programming language. Part of a larger series teaching programming. See http://codeschool.org
From playlist The C language
Geostatistics session 3 universal kriging
Introduction to Universal Kriging
From playlist Geostatistics GS240
12d Python Data Analytics: Simple Kriging
An interactive demonstration of simple kriging in Python. To follow along the Python Jupyter Notebook is available here: https://git.io/JfIpD.
From playlist Data Analytics and Geostatistics
Lecture on the motivation for simulation vs. estimation and development of the sequential Gaussian simulation approach.
From playlist Data Analytics and Geostatistics
12b Geostatistics Course: Kriging
Lecture on kriging for spatial estimation.
From playlist Data Analytics and Geostatistics
14 Data Analytics: Indicator Methods
Lecture on the use of indicators for spatial estimation and simulation.
From playlist Data Analytics and Geostatistics
Geostatistics session 5 conditional simulation
Introduction to conditional simulation with Gaussian processes
From playlist Geostatistics GS240
Tutorial: Open Source Spatial Data Analytics in Python with GeostatsPy
TRANSFORM 2020 - Virtual Conference Michael Pyrcz To access the repos link: https://swu.ng/t20-mon-geostats 0:28 Start of stream and intro 6:16 Intro to GeostatsPy 28:59 Setup 33:07 Variograms 1:50:18 Kriging 2:11:17 Start of break 2 2:17:26 End of break 2 2:27:48 Geostatspy kriging 2:
From playlist Random Talks