Chapman-Kolmogorov equation

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

p_{i_1,\ldots,i_n}(f_1,\ldots,f_n)

be the joint probability density function of the values of the random variables f1 to fn. Then, the Chapman-Kolmogorov equation is

p_{i_1,\ldots,i_{n-1}}(f_1,\ldots,f_{n-1})=\int_{-\infty}^{\infty}p_{i_1,\ldots,i_n}(f_1,\ldots,f_n)\,df_n

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 i_1<\ldots<i_n. Then, because of the Markov property,

p_{i_1,\ldots,i_n}(f_1,\ldots,f_n)=p_{i_1}(f_1)p_{i_2;i_1}(f_2\mid f_1)\cdots p_{i_n;i_{n-1}}(f_n\mid  f_{n-1}),

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

See also: Chapman-Kolmogorov equation, Andrey Kolmogorov, Examples of Markov chains, Markov chain, Markov property, Mathematics, Matrix multiplication, Probability theory, Stochastic process, Transition probability