In the theory of stochastic processes, a ν-transform is an operation that transforms a measure or a point process into a different point process.
Intuitively the ν-transform randomly relocates the points of the point process, with the type of relocation being dependent on the position of each point.
denote the Dirac measure on the point
be a simple point measure on
This means that for distinct
for every bounded set
be a Markov kernel from
be independent random elements with distribution
Then the point process is called the ν-transform of the measure
if it is locally finite, meaning that
for every bounded set
[1] For a point process
, a second point process
is called a
{ ξ = μ }
, the point process
is a Cox process directed by the random measure
is again a Cox-process, directed by the random measure
ξ ⋅ ν
(see Transition kernel#Composition of kernels)[2] Therefore, the
-transform of a Poisson process with intensity measure
is a Cox process directed by a random measure with distribution
μ ⋅ ν
, then the Laplace transform of
is given by for all bounded, positive and measurable functions