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From Wikipedia, the free encyclopedia

In probability theory, a Cox process, also known as a doubly stochastic Poisson process is a point process which is a generalization of a Poisson process where the time-dependent intensity is itself a stochastic process. The process is named after the statistician David Cox, who first published the model in 1955.[1]

Cox processes are used to generate simulations of spike trains (the sequence of action potentials generated by a neuron),[2] and also in financial mathematics where they produce a "useful framework for modeling prices of financial instruments in which credit risk is a significant factor."[3]

Definition

Let be a random measure.

A random measure is called a Cox process directed by , if is a Poisson process with intensity measure .

Here, is the conditional distribution of , given .

Laplace transform

If is a Cox process directed by , then has the Laplace transform

for any positive, measurable function .

See also

References

Notes
  1. ^ Cox, D. R. (1955). "Some Statistical Methods Connected with Series of Events". Journal of the Royal Statistical Society. 17 (2): 129–164. doi:10.2307/2983950.
  2. ^ Krumin, M.; Shoham, S. (2009). "Generation of Spike Trains with Controlled Auto- and Cross-Correlation Functions". Neural Computation. 21 (6): 1642–1664. doi:10.1162/neco.2009.08-08-847. PMID 19191596.
  3. ^ Lando, David (1998). "On cox processes and credit risky securities". Review of Derivatives Research. 2 (2–3): 99–120. doi:10.1007/BF01531332.
Bibliography


This page was last edited on 21 January 2019, at 05:05
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