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In [[probability theory]], a '''Markov kernel''' (or '''stochastic kernel''') is a map that plays the role, in the general theory of [[Markov process]]es, that the [[transition matrix]] does in the theory of Markov processes with a finite state space.<ref>{{cite doi|10.1016/S0014-4983(02)00023-2}}</ref><ref>{{cite doi|10.1007/978-1-4613-9308-5}}</ref>


== Formal definition ==


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Let <math>(X,\mathcal A)</math>, <math>(Y,\mathcal B)</math> be [[measurable space]]s. A ''Markov kernel'' with source <math>(X,\mathcal A)</math> and target <math>(Y,\mathcal B)</math> is a map <math>K</math> that associates to each point <math>x \in X</math> a [[probability measure]] <math>K(x)</math> on <math>(Y,\mathcal B)</math> such that, for every measurable set <math>B\in\mathcal B</math>, the map <math>x\mapsto K(x)(B)</math> is measurable with respect to the <math>\sigma</math>-algebra <math>\mathcal A</math>.
 
Let <math>\mathcal P(X,\mathcal A)</math> denote the set of all probability measures on the measurable
space <math>(X,\mathcal A)</math>. If <math>K</math> is a Markov kernel with source <math>(X,\mathcal A)</math> and target <math>(Y,\mathcal B)</math> then we can naturally associate to <math>K</math> a map <math>\widehat K:\mathcal P(X,\mathcal A)\to\mathcal P(Y,\mathcal B)</math> defined as follows: given <math>P</math> in <math>\mathcal P(X,\mathcal A)</math>, we set <math>\widehat K(P)(B)=\int_XK(x)(B)\,\mathrm dP(x)</math>, for all <math>B</math> in <math>\mathcal B</math>.
 
==Estimation==
 
The kernel can be estimated using [[kernel density estimation]].<ref>{{cite doi|10.1016/j.econlet.2009.08.012}}</ref>
 
== References ==
{{Reflist}}
* {{citation|first1=Heinz|last1=Bauer|title=Probability Theory|publisher=de Gruyter|year=1996|isbn=3-11-013935-9}}
:: §36. Kernels and semigroups of kernels
 
[[Category:Stochastic processes]]

Revision as of 16:10, 26 July 2013

In probability theory, a Markov kernel (or stochastic kernel) is a map that plays the role, in the general theory of Markov processes, that the transition matrix does in the theory of Markov processes with a finite state space.[1][2]

Formal definition

Let (X,𝒜), (Y,) be measurable spaces. A Markov kernel with source (X,𝒜) and target (Y,) is a map K that associates to each point xX a probability measure K(x) on (Y,) such that, for every measurable set B, the map xK(x)(B) is measurable with respect to the σ-algebra 𝒜.

Let 𝒫(X,𝒜) denote the set of all probability measures on the measurable space (X,𝒜). If K is a Markov kernel with source (X,𝒜) and target (Y,) then we can naturally associate to K a map K^:𝒫(X,𝒜)𝒫(Y,) defined as follows: given P in 𝒫(X,𝒜), we set K^(P)(B)=XK(x)(B)dP(x), for all B in .

Estimation

The kernel can be estimated using kernel density estimation.[3]

References

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§36. Kernels and semigroups of kernels