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Stochastic processes --- Markov processes --- 519.217 --- Markov processes. --- Markov chains --- Markov chains. --- 519.217 Markov processes
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Models, Genetic --- Markov Chains --- DNA --- Logistic Models --- genetics
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STOCHASTIC PROCESSES[STATIONARY-] --- MARKOV CHAINS --- RENEWAL THEORY[PROBABILITY] --- Stochastic processes --- COUNTING PROCESSES --- POISSON PROCESSES --- Probabilities
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Discrete mathematics --- Graph theory --- Graph theory. --- Circuits --- Markov chains --- Network analysis theory --- Network flows --- Network synthesis --- Paths --- Trees mathematics
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This book concerns continuous-time controlled Markov chains, also known as continuous-time Markov decision processes. They form a class of stochastic control problems in which a single decision-maker wishes to optimize a given objective function. This book is also concerned with Markov games, where two decision-makers (or players) try to optimize their own objective function. Both decision-making processes appear in a large number of applications in economics, operations research, engineering, and computer science, among other areas. An extensive, self-contained, up-to-date analysis of basic o
Markov processes. --- Analysis, Markov --- Chains, Markov --- Markoff processes --- Markov analysis --- Markov chains --- Markov models --- Models, Markov --- Processes, Markov --- Stochastic processes
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This Springer brief addresses the challenges encountered in the study of the optimization of time-nonhomogeneous Markov chains. It develops new insights and new methodologies for systems in which concepts such as stationarity, ergodicity, periodicity and connectivity do not apply. This brief introduces the novel concept of confluencity and applies a relative optimization approach. It develops a comprehensive theory for optimization of the long-run average of time-nonhomogeneous Markov chains. The book shows that confluencity is the most fundamental concept in optimization, and that relative optimization is more suitable for treating the systems under consideration than standard ideas of dynamic programming. Using confluencity and relative optimization, the author classifies states as confluent or branching and shows how the under-selectivity issue of the long-run average can be easily addressed, multi-class optimization implemented, and Nth biases and Blackwell optimality conditions derived. These results are presented in a book for the first time and so may enhance the understanding of optimization and motivate new research ideas in the area.
Markov processes. --- Analysis, Markov --- Chains, Markov --- Markoff processes --- Markov analysis --- Markov chains --- Markov models --- Models, Markov --- Processes, Markov --- Stochastic processes
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Breast Neoplasms --- Decision Support Techniques --- Health Status --- Patient Satisfaction --- Markov Chains --- Psychometrics --- Attitude to Health --- psychology --- methods
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Stochastic processes --- Probability --- Statistics --- Markov processes --- Markov, Processus de --- 519.217.2 --- 519.217 --- #SBIB:303H521 --- 519.2 --- Analysis, Markov --- Chains, Markov --- Markoff processes --- Markov analysis --- Markov chains --- Markov models --- Models, Markov --- Processes, Markov --- Markov chains. Processes with finite or countable set of states --- Methoden sociale wetenschappen: waarschijnlijkheid --- Probability. Mathematical statistics --- Markov processes. --- 519.2 Probability. Mathematical statistics --- 519.217 Markov processes --- 519.217.2 Markov chains. Processes with finite or countable set of states --- Probability.
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Stochastic processes --- Markov processes. --- Markov, Processus de --- Markov processes --- 519.233 --- Analysis, Markov --- Chains, Markov --- Markoff processes --- Markov analysis --- Markov chains --- Markov models --- Models, Markov --- Processes, Markov
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Stochastic processes --- Markov processes --- 519.217 --- Analysis, Markov --- Chains, Markov --- Markoff processes --- Markov analysis --- Markov chains --- Markov models --- Models, Markov --- Processes, Markov --- Markov processes. --- 519.217 Markov processes
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