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Applied Statistics and Data Science : Proceedings of Statistics 2021 Canada, Selected Contributions / Yogendra P. Chaubey ... [et al.] editors
| Applied Statistics and Data Science : Proceedings of Statistics 2021 Canada, Selected Contributions / Yogendra P. Chaubey ... [et al.] editors |
| Pubbl/distr/stampa | Cham, : Springer, 2021 |
| Descrizione fisica | ix, 159 p. : ill. ; 24 cm |
| Soggetto topico |
00B25 - Proceedings of conferences of miscellaneous specific interest [MSC 2020]
62-XX - Statistics [MSC 2020] 62R07 - Statistical aspects of big data and data science [MSC 2020] 62Pxx - Applications of statistics [MSC 2020] |
| Soggetto non controllato |
Actuarial mathematics
Computational statistics Data Analytics Data Mining Data science Environmental Statistics Probability and Statistics in Computer Science Small area estimation Statistical Methods Supply chain Survival analysis |
| Formato | Materiale a stampa  |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Record Nr. | UNICAMPANIA-VAN0274563 |
| Cham, : Springer, 2021 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Vanvitelli | ||
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Applied Statistics and Data Science : Proceedings of Statistics 2021 Canada, Selected Contributions / Yogendra P. Chaubey ... [et al.] editors
| Applied Statistics and Data Science : Proceedings of Statistics 2021 Canada, Selected Contributions / Yogendra P. Chaubey ... [et al.] editors |
| Pubbl/distr/stampa | Cham, : Springer, 2021 |
| Descrizione fisica | ix, 159 p. : ill. ; 24 cm |
| Soggetto topico |
00B25 - Proceedings of conferences of miscellaneous specific interest [MSC 2020]
62-XX - Statistics [MSC 2020] 62Pxx - Applications of statistics [MSC 2020] 62R07 - Statistical aspects of big data and data science [MSC 2020] |
| Soggetto non controllato |
Actuarial mathematics
Computational statistics Data Analytics Data Mining Data science Environmental Statistics Probability and Statistics in Computer Science Small area estimation Statistical Methods Supply chain Survival analysis |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Record Nr. | UNICAMPANIA-VAN00274563 |
| Cham, : Springer, 2021 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Vanvitelli | ||
| ||
Bayesian Optimization with Application to Computer Experiments / Tony Pourmohamad, Herbert K. H. Lee
| Bayesian Optimization with Application to Computer Experiments / Tony Pourmohamad, Herbert K. H. Lee |
| Autore | Pourmohamad, Tony |
| Pubbl/distr/stampa | Cham, : Springer, 2021 |
| Descrizione fisica | x, 104 p. : ill. ; 24 cm |
| Altri autori (Persone) | Lee, Herbert K. H. |
| Soggetto topico |
68-XX - Computer science [MSC 2020]
62-XX - Statistics [MSC 2020] |
| Soggetto non controllato |
Bayesian Inference
Bayesian Network Black box optimization Computer models Constrained optimization Gaussian processes Network models Probability and Statistics in Computer Science Sequential experimental design Simulator Surrogate modeling |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Record Nr. | UNICAMPANIA-VAN0274590 |
Pourmohamad, Tony
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| Cham, : Springer, 2021 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Vanvitelli | ||
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Bayesian Optimization with Application to Computer Experiments / Tony Pourmohamad, Herbert K. H. Lee
| Bayesian Optimization with Application to Computer Experiments / Tony Pourmohamad, Herbert K. H. Lee |
| Autore | Pourmohamad, Tony |
| Pubbl/distr/stampa | Cham, : Springer, 2021 |
| Descrizione fisica | x, 104 p. : ill. ; 24 cm |
| Altri autori (Persone) | Lee, Herbert K. H. |
| Soggetto topico |
62-XX - Statistics [MSC 2020]
68-XX - Computer science [MSC 2020] |
| Soggetto non controllato |
Bayesian Inference
Bayesian Network Black box optimization Computer models Constrained optimization Gaussian processes Network models Probability and Statistics in Computer Science Sequential experimental design Simulator Surrogate modeling |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Record Nr. | UNICAMPANIA-VAN00274590 |
|
Pourmohamad, Tony
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| Cham, : Springer, 2021 | ||
| 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 |
|
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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Vol. 1: Financial Statistics and Portfolio Analysis / John Lee, Cheng-Few Lee
| Vol. 1: Financial Statistics and Portfolio Analysis / John Lee, Cheng-Few Lee |
| Autore | Lee, John |
| Edizione | [2. ed] |
| Pubbl/distr/stampa | Cham, : Springer, 2022 |
| Descrizione fisica | xvi, 696 p. : ill. ; 24 cm |
| Altri autori (Persone) | Lee, Cheng-Few |
| Soggetto topico |
62-XX - Statistics [MSC 2020]
62P05 - Applications of statistics to actuarial sciences and financial mathematics [MSC 2020] 68N15 - Theory of programming languages [MSC 2020] 91Gxx - Actuarial science and mathematical finance [MSC 2020] |
| Soggetto non controllato |
Business Analytics
Business mathematics Mathematical Finance Probability and Statistics in Computer Science Python Quantitative Finance Statistical finance |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Record Nr. | UNICAMPANIA-VAN00277395 |
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Lee, John
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| Cham, : Springer, 2022 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Vanvitelli | ||
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Vol. 1: Financial Statistics and Portfolio Analysis/ John Lee, Cheng-Few Lee
| Vol. 1: Financial Statistics and Portfolio Analysis/ John Lee, Cheng-Few Lee |
| Autore | Lee, John |
| Edizione | [2. ed] |
| Pubbl/distr/stampa | Cham, : Springer, 2022 |
| Descrizione fisica | xvi, 696 p. : ill. ; 24 cm |
| Altri autori (Persone) | Lee, Cheng-Few |
| Soggetto non controllato |
Business Analytics
Business mathematics Mathematical Finance Probability and Statistics in Computer Science Python Quantitative Finance Statistical finance |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Record Nr. | UNICAMPANIA-VAN0277395 |
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Lee, John
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| Cham, : Springer, 2022 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Vanvitelli | ||
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Vol. 2: Financial Derivatives, Risk Management and Machine Learning / John Lee ... [et al.]
| Vol. 2: Financial Derivatives, Risk Management and Machine Learning / John Lee ... [et al.] |
| Edizione | [2. ed] |
| Pubbl/distr/stampa | Cham, : Springer, 2023 |
| Descrizione fisica | xv, 523 p. : ill. ; 24 cm |
| Soggetto topico |
62-XX - Statistics [MSC 2020]
68N15 - Theory of programming languages [MSC 2020] 91-XX - Game theory, economics, finance, and other social and behavioral sciences [MSC 2020] 91G70 - Statistical methods; risk measures [MSC 2020] |
| Soggetto non controllato |
Business Analytics
Business mathematics Mathematical Finance Probability and Statistics in Computer Science Python Quantitative Finance Statistical finance |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Record Nr. | UNICAMPANIA-VAN00278799 |
| Cham, : Springer, 2023 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Vanvitelli | ||
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