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Chapman-kolmogorov EquationIn 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 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 where the conditional probability p_{i;j}(f_i\mid f_j) is the transition probability between the times i>j). So, the Chapman-Kolmogorov equation takes the form - p_{i_3;i_1}(f_3\mid f_1)=\int_{-\infty}^\infty p_{i_3;i_2}(f_3\mid f_2)p_{i_2;i_1}(f_2\mid f_1)df_2.
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: - P(t+s)=P(t)P(s)
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 - P_{ij}(t)=P(X_t=j\mid X_0=i).
See also examples of Markov chains External links
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