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Markov processes for stochastic modeling [[electronic resource]] / Oliver C. Ibe
| Markov processes for stochastic modeling [[electronic resource]] / Oliver C. Ibe |
| Autore | Ibe Oliver C |
| Edizione | [2nd ed.] |
| Pubbl/distr/stampa | Amsterdam, Netherlands, : Elsevier, c2013 |
| Descrizione fisica | 1 online resource (515 p.) |
| Disciplina | 519.233 |
| Collana | Elsevier insights Markov processes for stochastic modeling |
| Soggetto topico |
Markov processes
Stochastic processes |
| Soggetto genere / forma | Electronic books. |
| ISBN | 0-12-407839-7 |
| Formato | Materiale a stampa  |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Front Cover; Markov Processes for Stochastic Modeling; Copyright page; Contents; Acknowledgments; Preface to the Second Edition; Preface to the First Edition; 1 Basic Concepts in Probability; 1.1 Introduction; 1.1.1 Conditional Probability; 1.1.2 Independence; 1.1.3 Total Probability and the Bayes' Theorem; 1.2 Random Variables; 1.2.1 Distribution Functions; 1.2.2 Discrete Random Variables; 1.2.3 Continuous Random Variables; 1.2.4 Expectations; 1.2.5 Expectation of Nonnegative Random Variables; 1.2.6 Moments of Random Variables and the Variance; 1.3 Transform Methods; 1.3.1 The s-Transform
1.3.2 The z-Transform1.4 Bivariate Random Variables; 1.4.1 Discrete Bivariate Random Variables; 1.4.2 Continuous Bivariate Random Variables; 1.4.3 Covariance and Correlation Coefficient; 1.5 Many Random Variables; 1.6 Fubini's Theorem; 1.7 Sums of Independent Random Variables; 1.8 Some Probability Distributions; 1.8.1 The Bernoulli Distribution; 1.8.2 The Binomial Distribution; 1.8.3 The Geometric Distribution; 1.8.4 The Pascal Distribution; 1.8.5 The Poisson Distribution; 1.8.6 The Exponential Distribution; 1.8.7 The Erlang Distribution; 1.8.8 Normal Distribution; 1.9 Limit Theorems 1.9.1 Markov Inequality1.9.2 Chebyshev Inequality; 1.9.3 Laws of Large Numbers; 1.9.4 The Central Limit Theorem; 1.10 Problems; 2 Basic Concepts in Stochastic Processes; 2.1 Introduction; 2.2 Classification of Stochastic Processes; 2.3 Characterizing a Stochastic Process; 2.4 Mean and Autocorrelation Function of a Stochastic Process; 2.5 Stationary Stochastic Processes; 2.5.1 Strict-Sense Stationary Processes; 2.5.2 Wide-Sense Stationary Processes; 2.6 Ergodic Stochastic Processes; 2.7 Some Models of Stochastic Processes; 2.7.1 Martingales; Stopping Times; 2.7.2 Counting Processes 2.7.3 Independent Increment Processes2.7.4 Stationary Increment Process; 2.7.5 Poisson Processes; Interarrival Times for the Poisson Process; Compound Poisson Process; Combinations of Independent Poisson Processes; Competing Independent Poisson Processes; Subdivision of a Poisson Process; 2.8 Problems; 3 Introduction to Markov Processes; 3.1 Introduction; 3.2 Structure of Markov Processes; 3.3 Strong Markov Property; 3.4 Applications of Discrete-Time Markov Processes; 3.4.1 Branching Processes; 3.4.2 Social Mobility; 3.4.3 Markov Decision Processes 3.5 Applications of Continuous-Time Markov Processes3.5.1 Queueing Systems; 3.5.2 Continuous-Time Markov Decision Processes; 3.5.3 Stochastic Storage Systems; 3.6 Applications of Continuous-State Markov Processes; 3.6.1 Application of Diffusion Processes to Financial Options; 3.6.2 Applications of Brownian Motion; 3.7 Summary; 4 Discrete-Time Markov Chains; 4.1 Introduction; 4.2 State-Transition Probability Matrix; 4.2.1 The n-Step State-Transition Probability; 4.3 State-Transition Diagrams; 4.4 Classification of States; 4.5 Limiting-State Probabilities; 4.5.1 Doubly Stochastic Matrix 4.6 Sojourn Time |
| Record Nr. | UNINA-9910459152303321 |
Ibe Oliver C
|
||
| Amsterdam, Netherlands, : Elsevier, c2013 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Markov processes for stochastic modeling / / Oliver C. Ibe, University of Massachusetts, Lowell, MA, USA
| Markov processes for stochastic modeling / / Oliver C. Ibe, University of Massachusetts, Lowell, MA, USA |
| Autore | Ibe Oliver C |
| Edizione | [2nd ed.] |
| Pubbl/distr/stampa | Amsterdam, Netherlands, : Elsevier, c2013 |
| Descrizione fisica | 1 online resource (xviii, 494 pages) : illustrations |
| Disciplina | 519.233 |
| Collana | Elsevier insights Markov processes for stochastic modeling |
| Soggetto topico |
Markov processes
Stochastic processes |
| ISBN | 0-12-407839-7 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Front Cover; Markov Processes for Stochastic Modeling; Copyright page; Contents; Acknowledgments; Preface to the Second Edition; Preface to the First Edition; 1 Basic Concepts in Probability; 1.1 Introduction; 1.1.1 Conditional Probability; 1.1.2 Independence; 1.1.3 Total Probability and the Bayes' Theorem; 1.2 Random Variables; 1.2.1 Distribution Functions; 1.2.2 Discrete Random Variables; 1.2.3 Continuous Random Variables; 1.2.4 Expectations; 1.2.5 Expectation of Nonnegative Random Variables; 1.2.6 Moments of Random Variables and the Variance; 1.3 Transform Methods; 1.3.1 The s-Transform
1.3.2 The z-Transform1.4 Bivariate Random Variables; 1.4.1 Discrete Bivariate Random Variables; 1.4.2 Continuous Bivariate Random Variables; 1.4.3 Covariance and Correlation Coefficient; 1.5 Many Random Variables; 1.6 Fubini's Theorem; 1.7 Sums of Independent Random Variables; 1.8 Some Probability Distributions; 1.8.1 The Bernoulli Distribution; 1.8.2 The Binomial Distribution; 1.8.3 The Geometric Distribution; 1.8.4 The Pascal Distribution; 1.8.5 The Poisson Distribution; 1.8.6 The Exponential Distribution; 1.8.7 The Erlang Distribution; 1.8.8 Normal Distribution; 1.9 Limit Theorems 1.9.1 Markov Inequality1.9.2 Chebyshev Inequality; 1.9.3 Laws of Large Numbers; 1.9.4 The Central Limit Theorem; 1.10 Problems; 2 Basic Concepts in Stochastic Processes; 2.1 Introduction; 2.2 Classification of Stochastic Processes; 2.3 Characterizing a Stochastic Process; 2.4 Mean and Autocorrelation Function of a Stochastic Process; 2.5 Stationary Stochastic Processes; 2.5.1 Strict-Sense Stationary Processes; 2.5.2 Wide-Sense Stationary Processes; 2.6 Ergodic Stochastic Processes; 2.7 Some Models of Stochastic Processes; 2.7.1 Martingales; Stopping Times; 2.7.2 Counting Processes 2.7.3 Independent Increment Processes2.7.4 Stationary Increment Process; 2.7.5 Poisson Processes; Interarrival Times for the Poisson Process; Compound Poisson Process; Combinations of Independent Poisson Processes; Competing Independent Poisson Processes; Subdivision of a Poisson Process; 2.8 Problems; 3 Introduction to Markov Processes; 3.1 Introduction; 3.2 Structure of Markov Processes; 3.3 Strong Markov Property; 3.4 Applications of Discrete-Time Markov Processes; 3.4.1 Branching Processes; 3.4.2 Social Mobility; 3.4.3 Markov Decision Processes 3.5 Applications of Continuous-Time Markov Processes3.5.1 Queueing Systems; 3.5.2 Continuous-Time Markov Decision Processes; 3.5.3 Stochastic Storage Systems; 3.6 Applications of Continuous-State Markov Processes; 3.6.1 Application of Diffusion Processes to Financial Options; 3.6.2 Applications of Brownian Motion; 3.7 Summary; 4 Discrete-Time Markov Chains; 4.1 Introduction; 4.2 State-Transition Probability Matrix; 4.2.1 The n-Step State-Transition Probability; 4.3 State-Transition Diagrams; 4.4 Classification of States; 4.5 Limiting-State Probabilities; 4.5.1 Doubly Stochastic Matrix 4.6 Sojourn Time |
| Record Nr. | UNINA-9910792485203321 |
|
Ibe Oliver C
|
||
| Amsterdam, Netherlands, : Elsevier, c2013 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Markov processes for stochastic modeling / / Oliver C. Ibe, University of Massachusetts, Lowell, MA, USA
| Markov processes for stochastic modeling / / Oliver C. Ibe, University of Massachusetts, Lowell, MA, USA |
| Autore | Ibe Oliver C |
| Edizione | [2nd ed.] |
| Pubbl/distr/stampa | Amsterdam, Netherlands, : Elsevier, c2013 |
| Descrizione fisica | 1 online resource (xviii, 494 pages) : illustrations |
| Disciplina | 519.233 |
| Collana | Elsevier insights Markov processes for stochastic modeling |
| Soggetto topico |
Markov processes
Stochastic processes |
| ISBN | 0-12-407839-7 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Front Cover; Markov Processes for Stochastic Modeling; Copyright page; Contents; Acknowledgments; Preface to the Second Edition; Preface to the First Edition; 1 Basic Concepts in Probability; 1.1 Introduction; 1.1.1 Conditional Probability; 1.1.2 Independence; 1.1.3 Total Probability and the Bayes' Theorem; 1.2 Random Variables; 1.2.1 Distribution Functions; 1.2.2 Discrete Random Variables; 1.2.3 Continuous Random Variables; 1.2.4 Expectations; 1.2.5 Expectation of Nonnegative Random Variables; 1.2.6 Moments of Random Variables and the Variance; 1.3 Transform Methods; 1.3.1 The s-Transform
1.3.2 The z-Transform1.4 Bivariate Random Variables; 1.4.1 Discrete Bivariate Random Variables; 1.4.2 Continuous Bivariate Random Variables; 1.4.3 Covariance and Correlation Coefficient; 1.5 Many Random Variables; 1.6 Fubini's Theorem; 1.7 Sums of Independent Random Variables; 1.8 Some Probability Distributions; 1.8.1 The Bernoulli Distribution; 1.8.2 The Binomial Distribution; 1.8.3 The Geometric Distribution; 1.8.4 The Pascal Distribution; 1.8.5 The Poisson Distribution; 1.8.6 The Exponential Distribution; 1.8.7 The Erlang Distribution; 1.8.8 Normal Distribution; 1.9 Limit Theorems 1.9.1 Markov Inequality1.9.2 Chebyshev Inequality; 1.9.3 Laws of Large Numbers; 1.9.4 The Central Limit Theorem; 1.10 Problems; 2 Basic Concepts in Stochastic Processes; 2.1 Introduction; 2.2 Classification of Stochastic Processes; 2.3 Characterizing a Stochastic Process; 2.4 Mean and Autocorrelation Function of a Stochastic Process; 2.5 Stationary Stochastic Processes; 2.5.1 Strict-Sense Stationary Processes; 2.5.2 Wide-Sense Stationary Processes; 2.6 Ergodic Stochastic Processes; 2.7 Some Models of Stochastic Processes; 2.7.1 Martingales; Stopping Times; 2.7.2 Counting Processes 2.7.3 Independent Increment Processes2.7.4 Stationary Increment Process; 2.7.5 Poisson Processes; Interarrival Times for the Poisson Process; Compound Poisson Process; Combinations of Independent Poisson Processes; Competing Independent Poisson Processes; Subdivision of a Poisson Process; 2.8 Problems; 3 Introduction to Markov Processes; 3.1 Introduction; 3.2 Structure of Markov Processes; 3.3 Strong Markov Property; 3.4 Applications of Discrete-Time Markov Processes; 3.4.1 Branching Processes; 3.4.2 Social Mobility; 3.4.3 Markov Decision Processes 3.5 Applications of Continuous-Time Markov Processes3.5.1 Queueing Systems; 3.5.2 Continuous-Time Markov Decision Processes; 3.5.3 Stochastic Storage Systems; 3.6 Applications of Continuous-State Markov Processes; 3.6.1 Application of Diffusion Processes to Financial Options; 3.6.2 Applications of Brownian Motion; 3.7 Summary; 4 Discrete-Time Markov Chains; 4.1 Introduction; 4.2 State-Transition Probability Matrix; 4.2.1 The n-Step State-Transition Probability; 4.3 State-Transition Diagrams; 4.4 Classification of States; 4.5 Limiting-State Probabilities; 4.5.1 Doubly Stochastic Matrix 4.6 Sojourn Time |
| Record Nr. | UNINA-9910815457703321 |
|
Ibe Oliver C
|
||
| Amsterdam, Netherlands, : Elsevier, c2013 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||