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Backward Stochastic Differential Equations : From Linear to Fully Nonlinear Theory / Jianfeng Zhang
| Backward Stochastic Differential Equations : From Linear to Fully Nonlinear Theory / Jianfeng Zhang |
| Autore | Zhang, Jianfeng |
| Pubbl/distr/stampa | New York, : Springer, 2017 |
| Descrizione fisica | xv, 386 p. ; 24 cm |
| Soggetto topico |
60H10 - Stochastic ordinary differential equations [MSC 2020]
60H30 - Applications of stochastic analysis (to PDEs, etc.) [MSC 2020] |
| Soggetto non controllato |
Backward Stochastic Differential Equations
Economic Theory, Quantitative Economics, Mathematical Methods Game Theory, Economics Social and Behavioral Science Mathematical Finance Nonlinear expectations Numerical Analysis Parabolic partial differential equations Partial differential equations Path Dependent Partial Differential Equations Probability Theory and Stochastic Processes Quantitative Finance Second Order Backward Stochastic Differential Equations Stochastic Controls Stochastic differential equations Viscosity solutions Weak Formulation |
| Formato | Materiale a stampa  |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Titolo uniforme | |
| Record Nr. | UNICAMPANIA-VAN0123804 |
Zhang, Jianfeng
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| New York, : Springer, 2017 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Vanvitelli | ||
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Backward Stochastic Differential Equations : From Linear to Fully Nonlinear Theory / Jianfeng Zhang
| Backward Stochastic Differential Equations : From Linear to Fully Nonlinear Theory / Jianfeng Zhang |
| Autore | Zhang, Jianfeng |
| Pubbl/distr/stampa | New York, : Springer, 2017 |
| Descrizione fisica | xv, 386 p. ; 24 cm |
| Soggetto topico |
60H10 - Stochastic ordinary differential equations [MSC 2020]
60H30 - Applications of stochastic analysis (to PDEs, etc.) [MSC 2020] |
| Soggetto non controllato |
Backward Stochastic Differential Equations
Economic Theory, Quantitative Economics, Mathematical Methods Game Theory, Economics Social and Behavioral Science Mathematical Finance Nonlinear expectations Numerical Analysis Parabolic partial differential equations Partial Differential Equations Path Dependent Partial Differential Equations Probability Theory and Stochastic Processes Quantitative Finance Second Order Backward Stochastic Differential Equations Stochastic Controls Stochastic differential equations Viscosity solutions Weak Formulation |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Titolo uniforme | |
| Record Nr. | UNICAMPANIA-VAN00123804 |
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Zhang, Jianfeng
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| New York, : Springer, 2017 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Vanvitelli | ||
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Elliptic Extensions in Statistical and Stochastic Systems / Makoto Katori
| Elliptic Extensions in Statistical and Stochastic Systems / Makoto Katori |
| Autore | Katori, Makoto |
| Pubbl/distr/stampa | Singapore, : Springer, 2023 |
| Descrizione fisica | xiv, 125 p. : ill. ; 24 cm |
| Soggetto topico |
33E05 - Elliptic functions and integrals [MSC 2020]
44-XX - Integral transforms, operational calculus [MSC 2020] 44A05 - General integral transforms [MSC 2020] 44A10 - Laplace transform [MSC 2020] 44A20 - Integral transforms of special functions [MSC 2020] 44A30 - Multiple integral transforms [MSC 2020] 44A45 - Classical operational calculus [MSC 2020] 60-XX - Probability theory and stochastic processes [MSC 2020] 60K35 - Interacting random processes; statistical mechanics type models; percolation theory [MSC 2020] 82C22 - Interacting particle systems in time-dependent statistical mechanics [MSC 2020] |
| Soggetto non controllato |
Brownian motion and lattice path models
Gaussian fields and point processes Probability Theory and Stochastic Processes Statistical physics and random matrix theory q-extensions and elliptic extensions |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Record Nr. | UNICAMPANIA-VAN00279189 |
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Katori, Makoto
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| Singapore, : Springer, 2023 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Vanvitelli | ||
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An Invitation to Statistics in Wasserstein Space [[electronic resource] /] / by Victor M. Panaretos, Yoav Zemel
| An Invitation to Statistics in Wasserstein Space [[electronic resource] /] / by Victor M. Panaretos, Yoav Zemel |
| Autore | Panaretos Victor M |
| Edizione | [1st ed. 2020.] |
| Pubbl/distr/stampa | Cham, : Springer Nature, 2020 |
| Descrizione fisica | 1 online resource (XIII, 147 p. 30 illus., 24 illus. in color.) |
| Disciplina | 519.2 |
| Collana | SpringerBriefs in Probability and Mathematical Statistics |
| Soggetto topico |
Probabilities
Probability Theory and Stochastic Processes |
| Soggetto non controllato |
Probability Theory and Stochastic Processes
Optimal Transportation Monge-Kantorovich Problem Barycenter Multimarginal Transport Functional Data Analysis Point Processes Random Measures Manifold Statistics Open Access Geometrical statistics Wasserstein metric Fréchet mean Procrustes analysis Phase variation Gradient descent Probability & statistics Stochastics |
| ISBN | 3-030-38438-1 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | Optimal transportation -- The Wasserstein space -- Fréchet means in the Wasserstein space -- Phase variation and Fréchet means -- Construction of Fréchet means and multicouplings. |
| Record Nr. | UNISA-996418267003316 |
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Panaretos Victor M
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| Cham, : Springer Nature, 2020 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. di Salerno | ||
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Matrix-exponential distributions in applied probability / Mogens Bladt, Bo Friis Nielsen
| Matrix-exponential distributions in applied probability / Mogens Bladt, Bo Friis Nielsen |
| Autore | Bladt, Mogens |
| Pubbl/distr/stampa | New York, : Springer, 2017 |
| Descrizione fisica | xvii, 736 p. : ill. ; 24 cm |
| Altri autori (Persone) | Nielsen, Bo Friis |
| Soggetto topico |
46-XX - Functional analysis [MSC 2020]
60-XX - Probability theory and stochastic processes [MSC 2020] |
| Soggetto non controllato |
Applied probability
Ladder processes Management Science Markov Processes Matrix exponential distributions Numerical methods Operations Research Phase-type distributions Probability Theory and Stochastic Processes Random Walks Regenerative methods Renewal theory Stochastic modeling Uncertainty Quantification |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Titolo uniforme | |
| Record Nr. | UNICAMPANIA-VAN0123376 |
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Bladt, Mogens
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| New York, : Springer, 2017 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Vanvitelli | ||
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Matrix-exponential distributions in applied probability / Mogens Bladt, Bo Friis Nielsen
| Matrix-exponential distributions in applied probability / Mogens Bladt, Bo Friis Nielsen |
| Autore | Bladt, Mogens |
| Pubbl/distr/stampa | New York, : Springer, 2017 |
| Descrizione fisica | xvii, 736 p. : ill. ; 24 cm |
| Altri autori (Persone) | Nielsen, Bo Friis |
| Soggetto topico |
46-XX - Functional analysis [MSC 2020]
60-XX - Probability theory and stochastic processes [MSC 2020] |
| Soggetto non controllato |
Applied probability
Ladder processes Management Science Markov Processes Matrix exponential distributions Numerical methods Operations Research Phase-type distributions Probability Theory and Stochastic Processes Random Walks Regenerative methods Renewal theory Stochastic modeling Uncertainty Quantification |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Titolo uniforme | |
| Record Nr. | UNICAMPANIA-VAN00123376 |
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Bladt, Mogens
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| New York, : Springer, 2017 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Vanvitelli | ||
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Probability in Electrical Engineering and Computer Science [[electronic resource] ] : An Application-Driven Course
| Probability in Electrical Engineering and Computer Science [[electronic resource] ] : An Application-Driven Course |
| Autore | Walrand Jean |
| Pubbl/distr/stampa | Cham, : Springer International Publishing AG, 2021 |
| Descrizione fisica | 1 online resource (390 p.) |
| Soggetto topico |
Maths for computer scientists
Communications engineering / telecommunications Maths for engineers Probability & statistics |
| Soggetto non controllato |
Probability and Statistics in Computer Science
Communications Engineering, Networks Mathematical and Computational Engineering Probability Theory and Stochastic Processes Statistics for Engineering, Physics, Computer Science, Chemistry and Earth Sciences Mathematical and Computational Engineering Applications Probability Theory Statistics in Engineering, Physics, Computer Science, Chemistry and Earth Sciences Applied probability Hypothesis testing Detection theory Expectation maximization Stochastic dynamic programming Machine learning Stochastic gradient descent Deep neural networks Matrix completion Linear and polynomial regression Open Access Maths for computer scientists Mathematical & statistical software Communications engineering / telecommunications Maths for engineers Probability & statistics Stochastics |
| ISBN | 3-030-49995-2 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Record Nr. | UNISA-996464521903316 |
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Walrand Jean
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| Cham, : Springer International Publishing AG, 2021 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. di Salerno | ||
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Probability in Electrical Engineering and Computer Science : An Application-Driven Course
| Probability in Electrical Engineering and Computer Science : An Application-Driven Course |
| Autore | Walrand Jean |
| Edizione | [1st ed.] |
| Pubbl/distr/stampa | Cham, : Springer International Publishing AG, 2021 |
| Descrizione fisica | 1 online resource (390 p.) |
| Soggetto topico |
Maths for computer scientists
Communications engineering / telecommunications Maths for engineers Probability & statistics |
| Soggetto non controllato |
Probability and Statistics in Computer Science
Communications Engineering, Networks Mathematical and Computational Engineering Probability Theory and Stochastic Processes Statistics for Engineering, Physics, Computer Science, Chemistry and Earth Sciences Mathematical and Computational Engineering Applications Probability Theory Statistics in Engineering, Physics, Computer Science, Chemistry and Earth Sciences Applied probability Hypothesis testing Detection theory Expectation maximization Stochastic dynamic programming Machine learning Stochastic gradient descent Deep neural networks Matrix completion Linear and polynomial regression Open Access Maths for computer scientists Mathematical & statistical software Communications engineering / telecommunications Maths for engineers Probability & statistics Stochastics |
| ISBN | 3-030-49995-2 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Intro -- Preface -- Acknowledgements -- Introduction -- About This Second Edition -- Contents -- 1 PageRank: A -- 1.1 Model -- 1.2 Markov Chain -- 1.2.1 General Definition -- 1.2.2 Distribution After n Steps and Invariant Distribution -- 1.3 Analysis -- 1.3.1 Irreducibility and Aperiodicity -- 1.3.2 Big Theorem -- 1.3.3 Long-Term Fraction of Time -- 1.4 Illustrations -- 1.5 Hitting Time -- 1.5.1 Mean Hitting Time -- 1.5.2 Probability of Hitting a State Before Another -- 1.5.3 FSE for Markov Chain -- 1.6 Summary -- 1.6.1 Key Equations and Formulas -- 1.7 References -- 1.8 Problems -- 2 PageRank: B -- 2.1 Sample Space -- 2.2 Laws of Large Numbers for Coin Flips -- 2.2.1 Convergence in Probability -- 2.2.2 Almost Sure Convergence -- 2.3 Laws of Large Numbers for i.i.d. RVs -- 2.3.1 Weak Law of Large Numbers -- 2.3.2 Strong Law of Large Numbers -- 2.4 Law of Large Numbers for Markov Chains -- 2.5 Proof of Big Theorem -- 2.5.1 Proof of Theorem 1.1 (a) -- 2.5.2 Proof of Theorem 1.1 (b) -- 2.5.3 Periodicity -- 2.6 Summary -- 2.6.1 Key Equations and Formulas -- 2.7 References -- 2.8 Problems -- 3 Multiplexing: A -- 3.1 Sharing Links -- 3.2 Gaussian Random Variable and CLT -- 3.2.1 Binomial and Gaussian -- 3.2.2 Multiplexing and Gaussian -- 3.2.3 Confidence Intervals -- 3.3 Buffers -- 3.3.1 Markov Chain Model of Buffer -- 3.3.2 Invariant Distribution -- 3.3.3 Average Delay -- 3.3.4 A Note About Arrivals -- 3.3.5 Little's Law -- 3.4 Multiple Access -- 3.5 Summary -- 3.5.1 Key Equations and Formulas -- 3.6 References -- 3.7 Problems -- 4 Multiplexing: B -- 4.1 Characteristic Functions -- 4.2 Proof of CLT (Sketch) -- 4.3 Moments of N(0, 1) -- 4.4 Sum of Squares of 2 i.i.d. N(0, 1) -- 4.5 Two Applications of Characteristic Functions -- 4.5.1 Poisson as a Limit of Binomial -- 4.5.2 Exponential as Limit of Geometric -- 4.6 Error Function.
4.7 Adaptive Multiple Access -- 4.8 Summary -- 4.8.1 Key Equations and Formulas -- 4.9 References -- 4.10 Problems -- 5 Networks: A -- 5.1 Spreading Rumors -- 5.2 Cascades -- 5.3 Seeding the Market -- 5.4 Manufacturing of Consent -- 5.5 Polarization -- 5.6 M/M/1 Queue -- 5.7 Network of Queues -- 5.8 Optimizing Capacity -- 5.9 Internet and Network of Queues -- 5.10 Product-Form Networks -- 5.10.1 Example -- 5.11 References -- 5.12 Problems -- 6 Networks-B -- 6.1 Social Networks -- 6.2 Continuous-Time Markov Chains -- 6.2.1 Two-State Markov Chain -- 6.2.2 Three-State Markov Chain -- 6.2.3 General Case -- 6.2.4 Uniformization -- 6.2.5 Time Reversal -- 6.3 Product-Form Networks -- 6.4 Proof of Theorem 5.7 -- 6.5 References -- 7 Digital Link-A -- 7.1 Digital Link -- 7.2 Detection and Bayes' Rule -- 7.2.1 Bayes' Rule -- 7.2.2 Circumstances vs. Causes -- 7.2.3 MAP and MLE -- Example: Ice Cream and Sunburn -- 7.2.4 Binary Symmetric Channel -- 7.3 Huffman Codes -- 7.4 Gaussian Channel -- Simulation -- 7.4.1 BPSK -- 7.5 Multidimensional Gaussian Channel -- 7.5.1 MLE in Multidimensional Case -- 7.6 Hypothesis Testing -- 7.6.1 Formulation -- 7.6.2 Solution -- 7.6.3 Examples -- Gaussian Channel -- Mean of Exponential RVs -- Bias of a Coin -- Discrete Observations -- 7.7 Summary -- 7.7.1 Key Equations and Formulas -- 7.8 References -- 7.9 Problems -- 8 Digital Link-B -- 8.1 Proof of Optimality of the Huffman Code -- 8.2 Proof of Neyman-Pearson Theorem 7.4 -- 8.3 Jointly Gaussian Random Variables -- 8.3.1 Density of Jointly Gaussian Random Variables -- 8.4 Elementary Statistics -- 8.4.1 Zero-Mean? -- 8.4.2 Unknown Variance -- 8.4.3 Difference of Means -- 8.4.4 Mean in Hyperplane? -- 8.4.5 ANOVA -- 8.5 LDPC Codes -- 8.6 Summary -- 8.6.1 Key Equations and Formulas -- 8.7 References -- 8.8 Problems -- 9 Tracking-A -- 9.1 Examples -- 9.2 Estimation Problem. 9.3 Linear Least Squares Estimates -- 9.3.1 Projection -- 9.4 Linear Regression -- 9.5 A Note on Overfitting -- 9.6 MMSE -- 9.6.1 MMSE for Jointly Gaussian -- 9.7 Vector Case -- 9.8 Kalman Filter -- 9.8.1 The Filter -- 9.8.2 Examples -- Random Walk -- Random Walk with Unknown Drift -- Random Walk with Changing Drift -- Falling Object -- 9.9 Summary -- 9.9.1 Key Equations and Formulas -- 9.10 References -- 9.11 Problems -- 10 Tracking: B -- 10.1 Updating LLSE -- 10.2 Derivation of Kalman Filter -- 10.3 Properties of Kalman Filter -- 10.3.1 Observability -- 10.3.2 Reachability -- 10.4 Extended Kalman Filter -- 10.4.1 Examples -- 10.5 Summary -- 10.5.1 Key Equations and Formulas -- 10.6 References -- 11 Speech Recognition: A -- 11.1 Learning: Concepts and Examples -- 11.2 Hidden Markov Chain -- 11.3 Expectation Maximization and Clustering -- 11.3.1 A Simple Clustering Problem -- 11.3.2 A Second Look -- 11.4 Learning: Hidden Markov Chain -- 11.4.1 HEM -- 11.4.2 Training the Viterbi Algorithm -- 11.5 Summary -- 11.5.1 Key Equations and Formulas -- 11.6 References -- 11.7 Problems -- 12 Speech Recognition: B -- 12.1 Online Linear Regression -- 12.2 Theory of Stochastic Gradient Projection -- 12.2.1 Gradient Projection -- 12.2.2 Stochastic Gradient Projection -- 12.2.3 Martingale Convergence -- 12.3 Big Data -- 12.3.1 Relevant Data -- 12.3.2 Compressed Sensing -- 12.3.3 Recommendation Systems -- 12.4 Deep Neural Networks -- 12.4.1 Calculating Derivatives -- 12.5 Summary -- 12.5.1 Key Equations and Formulas -- 12.6 References -- 12.7 Problems -- 13 Route Planning: A -- 13.1 Model -- 13.2 Formulation 1: Pre-planning -- 13.3 Formulation 2: Adapting -- 13.4 Markov Decision Problem -- 13.4.1 Examples -- 13.5 Infinite Horizon -- 13.6 Summary -- 13.6.1 Key Equations and Formulas -- 13.7 References -- 13.8 Problems -- 14 Route Planning: B -- 14.1 LQG Control. 14.1.1 Letting N →∞ -- 14.2 LQG with Noisy Observations -- 14.2.1 Letting N →∞ -- 14.3 Partially Observed MDP -- 14.3.1 Example: Searching for Your Keys -- 14.4 Summary -- 14.4.1 Key Equations and Formulas -- 14.5 References -- 14.6 Problems -- 15 Perspective and Complements -- 15.1 Inference -- 15.2 Sufficient Statistic -- 15.2.1 Interpretation -- 15.3 Infinite Markov Chains -- 15.3.1 Lyapunov-Foster Criterion -- 15.4 Poisson Process -- 15.4.1 Definition -- 15.4.2 Independent Increments -- 15.4.3 Number of Jumps -- 15.5 Boosting -- 15.6 Multi-Armed Bandits -- 15.7 Capacity of BSC -- 15.8 Bounds on Probabilities -- 15.8.1 Applying the Bounds to Multiplexing -- 15.9 Martingales -- 15.9.1 Definitions -- 15.9.2 Examples -- 15.9.3 Law of Large Numbers -- 15.9.4 Wald's Equality -- 15.10 Summary -- 15.10.1 Key Equations and Formulas -- 15.11 References -- 15.12 Problems -- Correction to: Probability in Electrical Engineering and Computer Science -- Correction to: Probability in Electrical Engineering and Computer Science (Funding Information) -- A Elementary Probability -- A.1 Symmetry -- A.2 Conditioning -- A.3 Common Confusion -- A.4 Independence -- A.5 Expectation -- A.6 Variance -- A.7 Inequalities -- A.8 Law of Large Numbers -- A.9 Covariance and Regression -- A.10 Why Do We Need a More Sophisticated Formalism? -- A.11 References -- A.12 Solved Problems -- B Basic Probability -- B.1 General Framework -- B.1.1 Probability Space -- B.1.2 Borel-Cantelli Theorem -- B.1.3 Independence -- B.1.4 Converse of Borel-Cantelli Theorem -- B.1.5 Conditional Probability -- B.1.6 Random Variable -- B.2 Discrete Random Variable -- B.2.1 Definition -- B.2.2 Expectation -- B.2.3 Function of a RV -- B.2.4 Nonnegative RV -- B.2.5 Linearity of Expectation -- B.2.6 Monotonicity of Expectation -- B.2.7 Variance, Standard Deviation. B.2.8 Important Discrete Random Variables -- B.3 Multiple Discrete Random Variables -- B.3.1 Joint Distribution -- B.3.2 Independence -- B.3.3 Expectation of Function of Multiple RVs -- B.3.4 Covariance -- B.3.5 Conditional Expectation -- B.3.6 Conditional Expectation of a Function -- B.4 General Random Variables -- B.4.1 Definitions -- B.4.2 Examples -- B.4.3 Expectation -- B.4.4 Continuity of Expectation -- B.5 Multiple Random Variables -- B.5.1 Random Vector -- B.5.2 Minimum and Maximum of Independent RVs -- B.5.3 Sum of Independent Random Variables -- B.6 Random Vectors -- B.6.1 Orthogonality and Projection -- B.7 Density of a Function of Random Variables -- B.7.1 Linear Transformations -- B.7.2 Nonlinear Transformations -- B.8 References -- B.9 Problems -- References -- Index. |
| Record Nr. | UNINA-9910488709003321 |
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Walrand Jean
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| Cham, : Springer International Publishing AG, 2021 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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Sample path analysis and distributions of boundary crossing times / Shelemyahu Zacks
| Sample path analysis and distributions of boundary crossing times / Shelemyahu Zacks |
| Autore | Zacks, Shelemyahu |
| Pubbl/distr/stampa | Cham, : Springer, 2017 |
| Descrizione fisica | XIII, 135 p. : ill. ; 24 cm |
| Soggetto topico |
60-XX - Probability theory and stochastic processes [MSC 2020]
60K40 - Other physical applications of random processes [MSC 2020] 60K20 - Applications of Markov renewal processes (reliability, queueing networks, etc.) [MSC 2020] 60K15 - Markov renewal processes, semi-Markov processes [MSC 2020] |
| Soggetto non controllato |
Applications in Queueing
Brownian Motions Compound Poisson Processes Compound Renewal Processes Distributions of First Crossing Times Inventory and Sequential Analysis Management Science Operations Research Poisson process Probability Theory and Stochastic Processes Renewal processes Telegraph Processes |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Titolo uniforme | |
| Record Nr. | UNICAMPANIA-VAN0114084 |
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Zacks, Shelemyahu
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| Cham, : Springer, 2017 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Vanvitelli | ||
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Sample path analysis and distributions of boundary crossing times / Shelemyahu Zacks
| Sample path analysis and distributions of boundary crossing times / Shelemyahu Zacks |
| Autore | Zacks, Shelemyahu |
| Pubbl/distr/stampa | Cham, : Springer, 2017 |
| Descrizione fisica | XIII, 135 p. : ill. ; 24 cm |
| Soggetto topico |
60-XX - Probability theory and stochastic processes [MSC 2020]
60K15 - Markov renewal processes, semi-Markov processes [MSC 2020] 60K20 - Applications of Markov renewal processes (reliability, queueing networks, etc.) [MSC 2020] 60K40 - Other physical applications of random processes [MSC 2020] |
| Soggetto non controllato |
Applications in Queueing
Brownian Motions Compound Poisson Processes Compound Renewal Processes Distributions of First Crossing Times Inventory and Sequential Analysis Management Science Operations Research Poisson process Probability Theory and Stochastic Processes Renewal processes Telegraph Processes |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Titolo uniforme | |
| Record Nr. | UNICAMPANIA-VAN00114084 |
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Zacks, Shelemyahu
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| Cham, : Springer, 2017 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Vanvitelli | ||
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