Matérn covariance function

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In statistics, the Matérn covariance, also called the Matérn kernel,[1] is a covariance function used in spatial statistics, geostatistics, machine learning, image analysis, and other applications of multivariate statistical analysis on metric spaces. It is named after the Swedish forestry statistician Bertil Matérn.[2] It specifies the covariance between two measurements as a function of the distance d between the points at which they are taken. Since the covariance only depends on distances between points, it is stationary. If the distance is Euclidean distance, the Matérn covariance is also isotropic.

Definition

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The Matérn covariance between measurements taken at two points separated by d distance units is given by [3]

Cν(d)=σ221νΓ(ν)(2νdρ)νKν(2νdρ),

where Γ is the gamma function, Kν is the modified Bessel function of the second kind, and ρ and ν are positive parameters of the covariance.

A Gaussian process with Matérn covariance is ν1 times differentiable in the mean-square sense.[3][4]

Spectral density

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The power spectrum of a process with Matérn covariance defined on n is the (n-dimensional) Fourier transform of the Matérn covariance function (see Wiener–Khinchin theorem). Explicitly, this is given by

S(f)=σ22nπn/2Γ(ν+n2)(2ν)νΓ(ν)ρ2ν(2νρ2+4π2f2)(ν+n2).[3]

Simplification for specific values of ν

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Simplification for ν half integer

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When ν=p+1/2, p+ , the Matérn covariance can be written as a product of an exponential and a polynomial of degree p.[5][6] The modified Bessel function of a fractional order is given by Equations 10.1.9 and 10.2.15[7] as

π2zKp+1/2(z)=π2zezk=0n(n+k)!k!Γ(nk+1)(2z)k .

This allows for the Matérn covariance of half-integer values of ν to be expressed as

Cp+1/2(d)=σ2exp(2p+1dρ)p!(2p)!i=0p(p+i)!i!(pi)!(22p+1dρ)pi,

which gives:

  • for ν=1/2 (p=0): C1/2(d)=σ2exp(dρ),
  • for ν=3/2 (p=1): C3/2(d)=σ2(1+3dρ)exp(3dρ),
  • for ν=5/2 (p=2): C5/2(d)=σ2(1+5dρ+5d23ρ2)exp(5dρ).

The Gaussian case in the limit of infinite ν

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As ν, the Matérn covariance converges to the squared exponential covariance function

limνCν(d)=σ2exp(d22ρ2).

Taylor series at zero and spectral moments

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From the basic relation satisfied by the Gamma function Γ(z)Γ(1z)=πsin(πz) and the basic relation satisfied by the Modified Bessel Function of the second

Kν(x)=π2Iν(x)Iν(x)sin(πν)

and the definition of the modified Bessel functions of the first Iν(x)=m=01m!Γ(m+ν+1)(x2)2m+ν,

the behavior for d0 can be obtained by the following Taylor series (when ν is not an integer and bigger than 2):

Cν(d)=σ2(1+ν2(1ν)(dρ)2+ν28(23ν+ν2)(dρ)4+𝒪(d6(2ν))),ν>2.[8]

When defined, the following spectral moments can be derived from the Taylor series:

λ0=Cν(0)=σ2,λ2=2Cν(d)d2|d=0=σ2νρ2(ν1).

For the case of ν(0,1)(1,2), similar Taylor series can be obtained: Cν(d)=σ2(1+ν2(1ν)(dρ)2Γ(1ν)Γ(1+ν)(ν2)ν(dρ)2ν+𝒪(d4(2ν+2))),ν(0,1)(1,2). When ν is an integer, limiting values should be taken (see [8]).

See also

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References

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  1. ^ Lua error in Module:Citation/CS1/Configuration at line 2172: attempt to index field '?' (a nil value).
  2. ^ Lua error in Module:Citation/CS1/Configuration at line 2172: attempt to index field '?' (a nil value).
  3. ^ a b c Rasmussen, Carl Edward and Williams, Christopher K. I. (2006) Gaussian Processes for Machine Learning
  4. ^ Santner, T. J., Williams, B. J., & Notz, W. I. (2013). The design and analysis of computer experiments. Springer Science & Business Media.
  5. ^ Stein, M. L. (1999). Interpolation of spatial data: some theory for kriging. Springer Series in Statistics.
  6. ^ Peter Guttorp & Tilmann Gneiting, 2006. "Studies in the history of probability and statistics XLIX On the Matern correlation family," Biometrika, Biometrika Trust, vol. 93(4), pages 989-995, December.
  7. ^ Lua error in Module:Citation/CS1/Configuration at line 2172: attempt to index field '?' (a nil value).
  8. ^ a b Lua error in Module:Citation/CS1/Configuration at line 2172: attempt to index field '?' (a nil value).