Markov Chains
-15%
portes grátis
Markov Chains
From Theory to Implementation and Experimentation
Gagniuc, Paul A.
John Wiley and Sons Ltd
07/2017
256
Dura
Inglês
9781119387558
15 a 20 dias
A fascinating and instructive guide to Markov chains for experienced users and newcomers alike This unique guide to Markov chains approaches the subject along the four convergent lines of mathematics, implementation, simulation, and experimentation.
Abstract Dedication Acknowledgements Preface Chapter 1: Historical Notes 1.1 Introduction 1.2 On The Wings Of Dependent Variables 1.3 From Bernoulli To Markov Chapter 2: From Observation To Simulation 2.1 Introduction 2.2 Stochastic Matrices 2.3 Transition Probabilities 2.4 The Simulation Of A Two-State Markov Chain Chapter 3: Building The Stochastic Matrix 3.1 Introduction 3.2 Building The Stochastic Matrix From Events 3.3 Building The Stochastic Matrix From Percentages Chapter 4: Predictions By Using 2-State Markov Chains 4.1 Introduction 4.2 Performing The Predictions By Using The Stochastic Matrix 4.3 The Steady State Of A Markov Chain 4.4 The Long-Run Distribution Of A Markov Chain Chapter 5: Predictions By Using N-State Markov Chains 5.1 Introduction 5.2 Predictions By Using The 3-State Markov Chain 5.3 Predictions By Using The 4-State Markov Chain 5.4 Predictions By Using N-State Markov Chains 5.5 Markov Chain Modeling On Measurements Chapter 6: Absorbing Markov Chains 6.1 Introduction 6.2 The Absorbing State Chapter 7: The Average Time Spent In Each State 7.1 Introduction 7.2 The Proportion Of Balls In The System 7.3 The Average Time Spent In A Particular State 7.4 Exemplification Of The Average Time And Proportions Chapter 8: Discussions On Different Configurations Of Chains 8.1 Introduction 8.2 Examples Of Two State Diagrams 8.3 Examples Of Three State Diagrams 8.4 Examples Of Four State Diagrams 8.5 Examples Of State Diagrams Divided Into Classes 8.6 Examples Of State Diagrams With Absorbing States 8.7 The Gambler's Ruin Chapter 9: The Simulation Of An N-State Markov Chain 9.1 Introduction 9.2 The Simulation Of Behavior 9.3 Simulation Of Different Chain Configurations Glossary Bibliography Appendices Appendix A: Supporting Algorithms In Php Appendix B: Supporting Algorithms In Javascript Appendix C: Syntax Equivalence Between Languages
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A fascinating and instructive guide to Markov chains for experienced users and newcomers alike This unique guide to Markov chains approaches the subject along the four convergent lines of mathematics, implementation, simulation, and experimentation.
Abstract Dedication Acknowledgements Preface Chapter 1: Historical Notes 1.1 Introduction 1.2 On The Wings Of Dependent Variables 1.3 From Bernoulli To Markov Chapter 2: From Observation To Simulation 2.1 Introduction 2.2 Stochastic Matrices 2.3 Transition Probabilities 2.4 The Simulation Of A Two-State Markov Chain Chapter 3: Building The Stochastic Matrix 3.1 Introduction 3.2 Building The Stochastic Matrix From Events 3.3 Building The Stochastic Matrix From Percentages Chapter 4: Predictions By Using 2-State Markov Chains 4.1 Introduction 4.2 Performing The Predictions By Using The Stochastic Matrix 4.3 The Steady State Of A Markov Chain 4.4 The Long-Run Distribution Of A Markov Chain Chapter 5: Predictions By Using N-State Markov Chains 5.1 Introduction 5.2 Predictions By Using The 3-State Markov Chain 5.3 Predictions By Using The 4-State Markov Chain 5.4 Predictions By Using N-State Markov Chains 5.5 Markov Chain Modeling On Measurements Chapter 6: Absorbing Markov Chains 6.1 Introduction 6.2 The Absorbing State Chapter 7: The Average Time Spent In Each State 7.1 Introduction 7.2 The Proportion Of Balls In The System 7.3 The Average Time Spent In A Particular State 7.4 Exemplification Of The Average Time And Proportions Chapter 8: Discussions On Different Configurations Of Chains 8.1 Introduction 8.2 Examples Of Two State Diagrams 8.3 Examples Of Three State Diagrams 8.4 Examples Of Four State Diagrams 8.5 Examples Of State Diagrams Divided Into Classes 8.6 Examples Of State Diagrams With Absorbing States 8.7 The Gambler's Ruin Chapter 9: The Simulation Of An N-State Markov Chain 9.1 Introduction 9.2 The Simulation Of Behavior 9.3 Simulation Of Different Chain Configurations Glossary Bibliography Appendices Appendix A: Supporting Algorithms In Php Appendix B: Supporting Algorithms In Javascript Appendix C: Syntax Equivalence Between Languages
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