An -superprocess, , within mathematics probability theory is a stochastic process on that is usually constructed as a special limit of near-critical branching diffusions.
Informally, it can be seen as a branching process where each particle splits and dies at infinite rates, and evolves according to a diffusion equation, and we follow the rescaled population of particles, seen as a measure on .
Scaling limit of a discrete branching process
Simplest setting
![](http://upload.wikimedia.org/wikipedia/commons/thumb/c/c5/Branching_Brownian_process..svg/220px-Branching_Brownian_process..svg.png)
For any integer , consider a branching Brownian process defined as follows:
- Start at with independent particles distributed according to a probability distribution .
- Each particle independently move according to a Brownian motion.
- Each particle independently dies with rate .
- When a particle dies, with probability it gives birth to two offspring in the same location.
The notation means should be interpreted as: at each time , the number of particles in a set is . In other words, is a measure-valued random process.[1]
Now, define a renormalized process:
Then the finite-dimensional distributions of converge as to those of a measure-valued random process , which is called a -superprocess,[1] with initial value , where and where is a Brownian motion (specifically, where is a measurable space, is a filtration, and under has the law of a Brownian motion started at ).
As will be clarified in the next section, encodes an underlying branching mechanism, and encodes the motion of the particles. Here, since is a Brownian motion, the resulting object is known as a Super-brownian motion.[1]
Generalization to (ξ, ϕ)-superprocesses
Our discrete branching system can be much more sophisticated, leading to a variety of superprocesses:
- Instead of , the state space can now be any Lusin space .
- The underlying motion of the particles can now be given by , where is a càdlàg Markov process (see,[1] Chapter 4, for details).
- A particle dies at rate
- When a particle dies at time , located in , it gives birth to a random number of offspring . These offspring start to move from . We require that the law of depends solely on , and that all are independent. Set and define the associated probability-generating function:
Add the following requirement that the expected number of offspring is bounded:Define as above, and define the following crucial function:Add the requirement, for all , that is Lipschitz continuous with respect to uniformly on , and that converges to some function as uniformly on .
Provided all of these conditions, the finite-dimensional distributions of converge to those of a measure-valued random process which is called a -superprocess,[1] with initial value .
Commentary on ϕ
Provided , that is, the number of branching events becomes infinite, the requirement that converges implies that, taking a Taylor expansion of , the expected number of offspring is close to 1, and therefore that the process is near-critical.
Generalization to Dawson-Watanabe superprocesses
The branching particle system can be further generalized as follows:
- The probability of death in the time interval of a particle following trajectory is where is a positive measurable function and is a continuous functional of (see,[1] chapter 2, for details).
- When a particle following trajectory dies at time , it gives birth to offspring according to a measure-valued probability kernel . In other words, the offspring are not necessarily born on their parent's location. The number of offspring is given by . Assume that .
Then, under suitable hypotheses, the finite-dimensional distributions of converge to those of a measure-valued random process which is called a Dawson-Watanabe superprocess,[1] with initial value .
Properties
A superprocess has a number of properties. It is a Markov process, and its Markov kernel verifies the branching property:where is the convolution.A special class of superprocesses are -superprocesses,[2] with . A -superprocesses is defined on . Its branching mechanism is defined by its factorial moment generating function (the definition of a branching mechanism varies slightly among authors, some[1] use the definition of in the previous section, others[2] use the factorial moment generating function):
and the spatial motion of individual particles (noted in the previous section) is given by the -symmetric stable process with infinitesimal generator .
The case means is a standard Brownian motion and the -superprocess is called the super-Brownian motion.
One of the most important properties of superprocesses is that they are intimately connected with certain nonlinear partial differential equations. The simplest such equation is When the spatial motion (migration) is a diffusion process, one talks about a superdiffusion. The connection between superdiffusions and nonlinear PDE's is similar to the one between diffusions and linear PDE's.
Further resources
- Eugene B. Dynkin (2004). Superdiffusions and positive solutions of nonlinear partial differential equations. Appendix A by J.-F. Le Gall and Appendix B by I. E. Verbitsky. University Lecture Series, 34. American Mathematical Society. ISBN 9780821836828.
References
- ^ a b c d e f g h Li, Zenghu (2011), Li, Zenghu (ed.), "Measure-Valued Branching Processes", Measure-Valued Branching Markov Processes, Berlin, Heidelberg: Springer, pp. 29–56, doi:10.1007/978-3-642-15004-3_2, ISBN 978-3-642-15004-3, retrieved 2022-12-20
- ^ a b Etheridge, Alison (2000). An introduction to superprocesses. Providence, RI: American Mathematical Society. ISBN 0-8218-2706-5. OCLC 44270365.