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In this study extending classical Markov chain theory to handle fluctuating transition matrices, the author develops a theory of Markov set-chains and provides numerous examples showing how that theory can be applied. Chapters are concluded with a discussion of related research. Readers who can benefit from this monograph are those interested in, or involved with, systems whose data is imprecise or that fluctuate with time. A background equivalent to a course in linear algebra and one in probability theory should be sufficient.

Stochastic processes --- Markov processes --- Stochastic matrices --- Mathematical Statistics --- Mathematical Theory --- Mathematics --- Physical Sciences & Mathematics --- Markoff processes --- Markov [Processus de ] --- Markov models --- Markov processen --- Markov-processen --- Processus de Markov --- Probabilities. --- Matrix theory. --- Algebra. --- Convex geometry . --- Discrete geometry. --- Biomathematics. --- Computer science—Mathematics. --- Probability Theory and Stochastic Processes. --- Linear and Multilinear Algebras, Matrix Theory. --- Convex and Discrete Geometry. --- Mathematical and Computational Biology. --- Math Applications in Computer Science. --- Biology --- Discrete mathematics --- Geometry --- Combinatorial geometry --- Mathematical analysis --- Probability --- Statistical inference --- Combinations --- Chance --- Least squares --- Mathematical statistics --- Risk