Fundamentals of Stochastic Models
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Fundamentals of Stochastic Models
Zhang, Zhe George
Taylor & Francis Ltd
12/2024
788
Mole
9780367712624
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1. Introduction. Part I. Fundamentals of Stochastic Models. 2. Discrete-time Markov Chains. 3. Continuous-Time Markov Chains. 4. Structured Markov Chains. 5. Renewal Processes and Embedded Markov Chains. 6. Random Walks and Brownian Motions. 7. Reflected Brownian Motion Approximations to Simple Stochastic Systems. 8. Large Queueing Systems. 9. Static Optimization in Stochastic Models. 10. Dynamic Optimization in Stochastic Models. 11. Learning in Stochastic Models. Part II. Appendices: Elements of Probability and Stochastics. A. Basics of Probability Theory. B. Conditional Expectation and Martingales. C. Some Useful Bounds, Inequalities, and Limit Laws. D. Non-linear Programming in Stochastics. E. Change of Probability Measure for a Normal Random Variable. F. Convergence of Random Variables. G. Major Theorems for Stochastic Process Limits. H. A Brief Review on Stochastic Calculus. I. Comparison of Stochastic Processes - Stochastic Orders. J. Matrix Algebra and Markov Chains.
Este título pertence ao(s) assunto(s) indicados(s). Para ver outros títulos clique no assunto desejado.
MAM;Matrix Analytical Methods;DFM;Diffusion and Fluid Limits Methods;Markov Decision Processes;Machine Learning;Constant Service Rate;Multi-dimensional State Spaces;General Arrival Processes;General Stochastic Systems;Semi-regenerative Processes;Markov Renewal Process;MDP;DTMC;CTMC;PH Distribution;MDP.;Structured Markov Chains;Continuous State Space;Markov Property;Markov Chain;Discrete State Space;Dynamic Optimization Problems;Inter-event Time
1. Introduction. Part I. Fundamentals of Stochastic Models. 2. Discrete-time Markov Chains. 3. Continuous-Time Markov Chains. 4. Structured Markov Chains. 5. Renewal Processes and Embedded Markov Chains. 6. Random Walks and Brownian Motions. 7. Reflected Brownian Motion Approximations to Simple Stochastic Systems. 8. Large Queueing Systems. 9. Static Optimization in Stochastic Models. 10. Dynamic Optimization in Stochastic Models. 11. Learning in Stochastic Models. Part II. Appendices: Elements of Probability and Stochastics. A. Basics of Probability Theory. B. Conditional Expectation and Martingales. C. Some Useful Bounds, Inequalities, and Limit Laws. D. Non-linear Programming in Stochastics. E. Change of Probability Measure for a Normal Random Variable. F. Convergence of Random Variables. G. Major Theorems for Stochastic Process Limits. H. A Brief Review on Stochastic Calculus. I. Comparison of Stochastic Processes - Stochastic Orders. J. Matrix Algebra and Markov Chains.
Este título pertence ao(s) assunto(s) indicados(s). Para ver outros títulos clique no assunto desejado.
MAM;Matrix Analytical Methods;DFM;Diffusion and Fluid Limits Methods;Markov Decision Processes;Machine Learning;Constant Service Rate;Multi-dimensional State Spaces;General Arrival Processes;General Stochastic Systems;Semi-regenerative Processes;Markov Renewal Process;MDP;DTMC;CTMC;PH Distribution;MDP.;Structured Markov Chains;Continuous State Space;Markov Property;Markov Chain;Discrete State Space;Dynamic Optimization Problems;Inter-event Time
