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1. |
Record Nr. |
UNINA9911019431603321 |
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Autore |
Berakdar J. <1964-> |
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Titolo |
Electronic correlation mapping : from finite to extended systems / / Jamal Berakdar |
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Pubbl/distr/stampa |
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Weinheim, : Wiley-VCH, c2006 |
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ISBN |
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9786611764500 |
9781281764508 |
1281764507 |
9783527618521 |
352761852X |
9783527618538 |
3527618538 |
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Descrizione fisica |
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1 online resource (207 p.) |
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Disciplina |
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Soggetti |
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Electron configuration |
Electronic excitation |
Electronic structure |
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Lingua di pubblicazione |
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Formato |
Materiale a stampa |
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Livello bibliografico |
Monografia |
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Note generali |
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Description based upon print version of record. |
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Nota di bibliografia |
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Includes bibliographical references and index. |
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Nota di contenuto |
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Electronic Correlation Mapping; Contents; 1 Qualitative and General Features of Electron-Electron Scattering; 1.1 MappingMomentum-distributionFunctions; 1.2 Role of Momentum Transfer during Electron-Electron Scattering; 1.3 Approximate Formula for the Electron-Electron Ionization Cross Section; 1.3.1 Example:AnAtomicTarget; 1.3.2 Electron-Electron Cross Section for Scattering from Condensed Matter; 1.3.3 Electron Scattering Cross Section from Ordered Materials; 1.3.4 Initial- vs. Final-state Interactions; 1.4 Averaged Electron-Electron Scattering Probabilities |
1.4.1 Integrated Cross Section for Strongly Localized States1.4.2 Low-energy Regime; 1.5 Electron-Electron Scattering in an Extended System; 2 Spin-effects on the Correlated Two-electron Continuum; 2.1 Generalities on the Spin-resolved Two-electron Emission; 2.2 Formal Symmetry Analysis; 2.3 Parametrization of the Spin-resolved Cross Sections; 2.4 Exchange-induced Spin Asymmetry; 2.5 Physical |
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Interpretation of the Exchange-induced Spin Asymmetry; 2.6 Spin Asymmetry in Correlated Two-electron Emission from Surfaces; 2.7 General Properties of the Spin Asymmetry |
2.7.1 Spin AsymmetryinPair Emissionfrom Bulk Matter2.7.2 Spin-polarized Homogenous Electron Gas; 2.7.3 Behavior of the Exchange-induced Spin Asymmetry in Scattering from Atomic Systems; 2.7.4 Threshold Behavior of the Spin Asymmetry; 3 Mechanisms of Correlated Electron Emission; 3.1 Exterior Complex Scaling; 3.2 The Convergent Close Coupling Method; 3.3 Analytical Models; 3.3.1 Dynamical Screening; 3.3.2 Influence of the Density of Final States; 3.4 Analysis of the Measured Angular Distributions; 3.4.1 The Intermediate Energy Regime |
3.5 Characteristics of the Correlated Pair Emissionat Low Energies3.5.1 Influence of the Exchange Interaction on the Angular Pair Correlation; 3.6 Threshold Behavior of the Energy and the Angular Pair Correlation; 3.6.1 Generalities of Threshold Pair Emission; 3.6.2 Threshold Pair Emissionfroma Coulomb Potential; 3.6.3 Regularities of the Measured Pair Correlation at Low Energies; 3.6.4 Role of Final-state Interactions in Low-energy Correlated Pair Emission; 3.6.5 Interpretation of Near-threshold Experiments; 3.7 Remarks on the Mechanisms of Electron-pair Emission from Atomic Systems |
4 Electron-electron Interaction in Extended Systems4.1 Exchange and Correlation Hole; 4.2 Pair-correlation Function; 4.2.1 Effect of Exchange on the Two-particle Probability Density; 4.3 Momentum-space Pair Densityand Two-particle Spectroscopy; 4.3.1 The S Matrix Elements; 4.3.2 Transition Probabilities and Cross Sections; 4.3.3 Two-particle Emissionand the Pair-correlation Function; 5 The Electron-Electron Interaction in Large Molecules and Clusters; 5.1 Retardation and Nonlocality of the Electron-Electron Interaction in Extended Systems; 5.2 Electron Emission from Fullerenes and Clusters |
5.2.1 The Spherical Jellium Model |
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Sommario/riassunto |
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An up-to-date selection of applications of correlation spectroscopy, in particular as far as the mapping of properties of correlated many-body systems is concerned. The book starts with a qualitative analysis of the outcome of the two-particle correlation spectroscopy of localized and delocalized electronic systems as they occur in atoms and solids. The second chapter addresses how spin-dependent interactions can be imaged by means of correlation spectroscopy, both in spin-polarized and extended systems. A further chapter discusses possible pathways for the production of interacting two-pa |
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2. |
Record Nr. |
UNINA9910483443303321 |
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Autore |
Dixon Matthew F. |
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Titolo |
Machine Learning in Finance : From Theory to Practice / / by Matthew F. Dixon, Igor Halperin, Paul Bilokon |
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Pubbl/distr/stampa |
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Cham : , : Springer International Publishing : , : Imprint : Springer, , 2020 |
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ISBN |
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Edizione |
[1st ed. 2020.] |
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Descrizione fisica |
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1 online resource (XXV, 548 p. 97 illus., 83 illus. in color.) |
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Disciplina |
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Soggetti |
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Statistics |
Applied mathematics |
Engineering mathematics |
Statistics for Business, Management, Economics, Finance, Insurance |
Applications of Mathematics |
Statistics, general |
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Lingua di pubblicazione |
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Formato |
Materiale a stampa |
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Livello bibliografico |
Monografia |
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Nota di bibliografia |
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Includes bibliographical references and index. |
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Nota di contenuto |
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Chapter 1. Introduction -- Chapter 2. Probabilistic Modeling -- Chapter 3. Bayesian Regression & Gaussian Processes -- Chapter 4. Feed Forward Neural Networks -- Chapter 5. Interpretability -- Chapter 6. Sequence Modeling -- Chapter 7. Probabilistic Sequence Modeling -- Chapter 8. Advanced Neural Networks -- Chapter 9. Introduction to Reinforcement learning -- Chapter 10. Applications of Reinforcement Learning -- Chapter 11. Inverse Reinforcement Learning and Imitation Learning -- Chapter 12. Frontiers of Machine Learning and Finance. |
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Sommario/riassunto |
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This book introduces machine learning methods in finance. It presents a unified treatment of machine learning and various statistical and computational disciplines in quantitative finance, such as financial econometrics and discrete time stochastic control, with an emphasis on how theory and hypothesis tests inform the choice of algorithm for financial data modeling and decision making. With the trend towards increasing computational resources and larger datasets, machine learning has grown into an important skillset for the finance industry. |
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This book is written for advanced graduate students and academics in financial econometrics, mathematical finance and applied statistics, in addition to quants and data scientists in the field of quantitative finance. Machine Learning in Finance: From Theory to Practice is divided into three parts, each part covering theory and applications. The first presents supervised learning for cross-sectional data from both a Bayesian and frequentist perspective. The more advanced material places a firm emphasis on neural networks, including deep learning, as well as Gaussian processes, with examples in investment management and derivative modeling. The second part presents supervised learning for time series data, arguably the most common data type used in finance with examples in trading, stochastic volatility and fixed income modeling. Finally, the third part presents reinforcement learning and its applications in trading, investment and wealth management. Python code examples are provided to support the readers' understanding of the methodologies and applications. The book also includes more than 80 mathematical and programming exercises, with worked solutions available to instructors. As a bridge to research in this emergent field, the final chapter presents the frontiers of machine learning in finance from a researcher's perspective, highlighting how many well-known concepts in statistical physics are likely to emerge as important methodologies for machine learning in finance. |
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