In this paper we consider a class of quasi-birth-and-death processes for which explicit solutions can be obtained for the rate matrix R and the associated matrix G. The probabilistic interpretations of these matrices allow us to describe their elements in terms of paths on the two-dimensional lattice. Then determining explicit expressions for the matrices becomes equivalent to solving a lattice path counting problem, the solution of which is derived using path decomposition, Bernoulli excursions, and hypergeometric functions. A few applications are provided, including classical models for which we obtain some new results.

The notion of complete level crossing information, or LCI-completeness, is introduced for quasi-birth-death (QBD) processes. It is shown that state space expansions allow any QBD-process to be modified so that it is LCI-complete.

For any LCI-complete, QBD-process, there exists a matrix W such that , where is the vector of limiting probabilities for all states on level n of the process. When W cannot be found in closed form, it can be found via an algorithm requiring fewer than m steps, where m is the number of states on each level of the process. The result of this algorithm is always a linear matrix equation for which W is the solution.

In essentially all cases considered in this paper, the matrix W is a solution of the matrix quadratic

X2A2 + XA1 + A0 = 0.

Despite this fact, W is never equal to Neuts' rate matrix R, although the non-zero eigenvalues and the corresponding left eigenvectors of R are a subset of the eigenvalues and left eigenvectors of W. This fact leads to two methods for determining R from W.

If the transition rates of the QBD-process are level-dependent, then it is also shown that matrices W(n) exist such that .

The problem considered is that of estimating the limit probability distribution (equilibrium distribution) πof a denumerable continuous time Markov process using only the matrix Q of derivatives of transition functions at the origin. We utilise relationships between the limit vector πand invariant measures for the jump-chain of the process (whose transition matrix we write P∗), and apply truncation theorems from Tweedie (1971) to P∗. When Q is regular, we derive algorithms for estimating πfrom truncations of Q; these extend results in Tweedie (1971), Section 4, from q-bounded processes to arbitrary regular processes. Finally, we show that this method can be extended even to non-regular chains of a certain type.