- Chapman-Kolmogorov equation
In
mathematics , specifically inprobability theory , and yet more specifically in the theory of Markovianstochastic process es, the Chapman-Kolmogorov equation can be viewed as an identity relating the joint probability distributions of different sets of coordinates on a stochastic process.These equations are pivotal in the study of this field, and they were worked out independently by the British mathematician Sydney Chapman and the Russian mathematician Andrey Kolmogorov. To give some idea of their importance, they are just as important, or more so, than the
Cauchy-Riemann equations in the subject of complex variables.Suppose that { "f""i" } is an indexed collection of random variables, that is, a stochastic process. Let
:
be the joint probability density function of the values of the random variables "f1" to "fn". Then, the Chapman-Kolmogorov equation is
:
i.e. a straightforward marginalization over the
nuisance variable .(Note that we have not yet assumed anything about the temporal (or any other) ordering of the random variables -- the above equation applies equally to the marginalization of any of them).
Particularization to Markov chains
When the stochastic process under consideration is Markovian, the Chapman-Kolmogorov equation is equivalent to an identity on transition densities. In the Markov chain setting, one assumes that
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