Transition kernel

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In the mathematics of probability, a transition kernel or kernel is a function in mathematics that has different applications. Kernels can for example be used to define random measures or stochastic processes. The most important example of kernels are the Markov kernels.

Let , be two measurable spaces. A function

is called a (transition) kernel from to if the following two conditions hold:[1]

  • For any fixed , the mapping
is -measurable;
  • For every fixed , the mapping
is a measure on .

Classification of transition kernels

Transition kernels are usually classified by the measures they define. Those measures are defined as

with

for all and all . With this notation, the kernel is called[1][2]

  • a substochastic kernel, sub-probability kernel or a sub-Markov kernel if all are sub-probability measures
  • a Markov kernel, stochastic kernel or probability kernel if all are probability measures
  • a finite kernel if all are finite measures
  • a -finite kernel if all are -finite measures
  • a -finite kernel if can be written as a countable sum of finite kernels (so that in particular, all are -finite measures).
  • a uniformly -finite kernel if there are at most countably many measurable sets in with for all and all .

Operations

Kernels as operators

References

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