Doob's theorem gives probability densities for a process that is both Gaussian and this.
Some integral equations can be solved by Smith's theorem for processes of this type, allowing evaluation of some of their associated densities. Doob's theorem gives probability densities for a process that is both Gaussian and this. The inequality of this name applies to random non-negative variables and leads to Chebyshev's inequality. The probability density for a sequence of this kind is given by the Chapman-Kolmogorov equation. For a finite state space, the transition probabilities are given by a stochastic matrix and depend only on the most recent state in the chains named for, FTP, this Russian mathematician.
Some integral equations can be solved by Smith's theorem for processes of this type, allowing evaluation of some of their associated densities.
{Markov} (accept {Markov} processes, {Markov} sequences, {Markov} matrices, {Markov} chains, etc.)