Probability theory and stochastic processes Definition
Excessive function
Definition
Let be a measurable space. Let be a sub-Markov transition semigroup on . That is, each is a kernel as for a Markov kernel, except that the total mass satisfies , is the identity, and . For a measurable , write . A measurable function is excessive for if:
- for every .
- as , for every .
Remarks
Under clause 1, is decreasing, since ; so the limit in clause 2 exists and is at most . A function with clause 1 alone is called supermedian; for , the -excessive functions are those that are excessive for the semigroup . Example: let be the semigroup of Brownian motion in and , with . Then is a nonnegative superharmonic function, and averaging it against the Gaussian kernel gives with as , so is excessive. Non-example: for the same semigroup, let be the indicator of the single point . Then , so clause 1 holds, but does not tend to , so clause 2 fails. For Brownian motion in , the excessive functions are exactly the nonnegative superharmonic functions on , together with the constant .
Reading path
To understand this page from the ground up, read these 4 pages in order. The foundations come first.
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- no evidence level
- Markov kernel concept
- Superharmonic function concept
- Wiener process concept
- Excessive function definition
Further reading
About this page
| Tag | BWMS6B1M5N |
|---|---|
| URI | m2://BWMS6B1M5N |
| Kind | definition |
| Writing | W0 stub |
| MSC | 60J45, 31D05 |
| Defines | Excessive function |
| Uses | Markov kernel, Wiener process, Superharmonic function |
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