  The standard approach in solving stochastic equations is eigenvector
decomposition. Using separation ansatz $P(i,t)=u(i)e^{\mu t}$ one obtains
standard equation for eigenvectors $Ku=\mu u$, where $K$ is the rate matrix of
the master equation. While universally accepted, the standard approach is not
the only possibility. Using additive separation ansatz $S(i,t)=W(i)-\nu t$ one
arrives at additive eigenvectors. Here we suggest a theory of such
eigenvectors. We argue that additive eigenvectors describe conditioned Markov
processes and derive corresponding equations. The formalism is applied to
one-dimensional stochastic process corresponding to the telegraph equation. We
derive differential equations for additive eigenvectors and explore their
properties. The proposed theory of additive eigenvectors provides a new
description of stochastic processes with peculiar properties.
