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In mathematics, specifically in probability theory, and yet more specifically in the theory of stochastic processes, the Chapman-Kolmogorov equation is an identity relating the joint probability distributions of different sets of coordinates on a stochastic process. Suppose that { fi } 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
Particularization to Markov chainsWhen 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 <math>i_1<\ldots
f_{n-1}),<math> where the conditional probability <math>p_{i;j}(f_i\mid f_j)<math> is the transition probability between the times <math>i>j<math>). So, the Chapman-Kolmogorov equation takes the form
When the probability distribution on the state space of a Markov chain is discrete, the Chapman-Kolmogorov equations can be expressed in terms of (possibly infinite-dimensional) matrix multiplication, thus:
where P(t) is the transition matrix, i.e., if Xt is the state of the process at time t, then for any two points i and j in the state space, we have
See alsoExternal links
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