High Quality Content by WIKIPEDIA articles! The chain rule for Kolmogorov complexity is an analogue of the chain rule for information entropy, which states: H(X,Y) = H(X) + H(Y | X). That is, the combined randomness of two sequences X and Y is the sum of the randomness of X plus whatever randomness is left in Y once we know X. This follows immediately from the definitions of conditional and joint entropy fact from probability theory that the joint probability is the product of the marginal and conditional probability: The equivalent statement for Kolmogorov complexity does not hold exactly; it is only true up to a logarithmic factor: K(X,Y) = K (X) + K(Y | X) + O(log(K(X,Y)))

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