Statistical Analysis Stack Exchange
Statistical Analysis Stack Exchange

Questions tagged [kriging]

Kriging is spatial prediction based on a stochastic model of a spatial random field. Such models and methods can also be used in non-spatial context, and then often known as gaussian processes.

Wikipedia has an article https://en.wikipedia.org/wiki/Kriging with further references.

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Splines vs Gaussian Process Regression

I'm know that Gaussian Process Regression (GPR) is an alternative to using splines for fitting flexible nonlinear models. I would like to know in which situations would one be more suitable than the other, especially in the Bayesian regression…
ved
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Ordinary kriging example step by step?

I have followed tutorials online for spatial kriging with both geoR and gstat (and also automap). I can perform spatial kriging and I understand the main concepts behind it. I know how to build a semivariogram, how to fit a model to it and how to…
Pigna
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How Does Kriging Interpolation work?

I am working on a problem in which I need to use Kriging to predict the value of some variables based on some surrounding variables. I want to implement its code by myself. So, I've went through too many documents to understand how it works, but I…
Dania
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What is the nugget effect?

I don't understand exactly what is meant by the term "nugget effect" in geostatistics. When looking at empirical variograms plotting the variogram $\gamma(h)$ vs. the lag $h$, the nugget is defined as the discontinuity from the origin when the lag…
MachineEpsilon
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What is the difference between a non-zero nugget and a noise term in Kriging/GPR?

With some Gaussian Process Regression/Kriging models, it's possible to specify both a non-zero nugget, and a noise term. For example, in Scikit-learn's GPR model, there is an alpha parameter, which I think represents the nugget, and a WhiteKernel…
naught101
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Gaussian process regression: leave-one-out prediction

According to Dubrule's Cross validation of kriging in a unique neighborhood, it is possible to compute leave-one-out the gaussian process prediction $\hat{Y}_{-i}(x_i)$ at a point $x_i$ from the design of experiments $\mathcal{X}$, where the kriging…
RobertoRonaldo
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Is kriging suitable for high dimensional regression problems?

I would like to point out that I am new to this field, so if I am not clear please forgive me (and correct me). I set up a DoE (Design of Experiment) with 11 inputs and 121 runs. I used a STOA (Strength-Two Orthogonal Array) to fill the domain and I…
NewGuest
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Is it valid to use a model-variogram fit not on the full range of lag distance?

I am trying to implement a form of 2 stage least squares, in step 1 I ignored the spatial correlation between the observations, now in step 2 I look at the spatial correlation of the residuals of step 1. Observations are spread around in an area of…
Kasper
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Who first proposed Bayesian optimisation with Gaussian processes?

From what I understand, the 'standard' approach to Bayesian Optimisation uses a Gaussian process for the prior (as opposed to more recent proposals like TPE or Bayesian Optimisation with random forests; please correct me if any of this is…
oulenz
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Differences between Kriging and Gaussian Process Regression

I am having quite difficult time to clearly understand the differences between Kriging and Gaussian Process Regression. Here is what I have understood so far: For simple kriging (mean value known), the two methods give the same result expect it is…
Akusa
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How do I estimate the prediction interval of back transformed log-normal data from Gaussian process?

I have some data that are clearly positively skewed and follow a log-normal distribution, lets assume the initial data is $Z = exp(Y)$, where $Y \sim N(\mu,\sigma^2)$. A Gaussian process assumes that any subsets from the data is normally…
Mahshrp
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what is the difference between Bayesian optimization and kriging?

Both methods use Gaussian process, and kriging uses the Best Linear Unbiased Predictor (BLUP) to predict the mean (this is not seen in Bayesian optimization?). At the bottom line, they also have covariance matrix, whose inverse has to be computed…
kensaii
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Why is positive definiteness necessary for kriging?

I understand from wikipedia that a variogram model must be positive definite to be used for kriging: Note that the experimental variogram is an empirical estimate of the covariance of a Gaussian process. As such, it may not be positive definite and…
makansij
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Gstat: Modelled semivariogram values not matching plotted model using the variogramLine function

I am trying to extract the semivariance values associated with a given semivariogram model developed in gstat, the end goal being to compare modelled semivariance with observed semivariance at defined distances. Using the function variogramLine to…
ChrisWills
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Universal kriging: which variogram to use?

I am working with the build in dataset meuse, which has 155 measurements of Zinc and the distance to the river "Meuse".(http://rspatial.r-forge.r-project.org/gallery/). Now I am trying to imitate universal kriging. In R, you first model a…
Kasper
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