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Recognized as a powerful tool for dealing with uncertainty, Markov modeling can enhance your ability to analyze complex production and service systems. However, most books on Markov chains or decision processes are often either highly theoretical, with few examples, or highly prescriptive, with little justification for the steps of the algorithms.
Dynamic Probabilistic Systems, Volume II
Dynamic Probabilistic Systems, Volume I
Decision Making Under Uncertainty
Analyzing Risk through Probabilistic Modeling in Operations Research
Markov Processes for Stochastic Modeling
Advanced Models and Tools for Effective Decision Making Under Uncertainty and Risk Contexts
Uncertainty Modeling and Analysis in Engineering and the Sciences
What Every Engineer Should Know About Decision Making Under Uncertainty
Markov Decision Processes
Modeling with Stochastic Programming
Semi-Markov Processes
Fundamentals of Uncertainty Quantification for Engineers