  Given a unichain Markov reward process (MRP), we provide an explicit
expression for the bias values in terms of mean first passage times. This
result implies a generalization of known Markov chain perturbation bounds for
the stationary distribution to the case where the perturbed chain is not
irreducible. It further yields an improved perturbation bound in 1-norm. As a
special case, Kemeny's constant can be interpreted as the translated bias in an
MRP with constant reward 1, which offers an intuitive explanation why it is a
constant.
