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[null,"1.","Record Nr.",null,"UNINA9910483845903321"]
[null,null,"Autore",null,"Sarmas Elissaios"]
[null,null,"Titolo",null,"Multicriteria Portfolio Construction with Python / / by Elissaios Sarmas, Panos Xidonas, Haris Doukas"]
[null,null,"Pubbl/distr/stampa",null,"Cham : , : Springer International Publishing : , : Imprint : Springer, , 2020"]
[null,null,"ISBN",null,"3-030-53743-9"]
[null,null,"Edizione",null,"[1st ed. 2020.]"]
[null,null,"Descrizione fisica",null,"1 online resource (IX, 176 p. 138 illus., 47 illus. in color.)"]
[null,null,"Collana",null,"Springer Optimization and Its Applications, , 1931-6836 ; ; 163"]
[null,null,"Disciplina",null,"332.60285"]
[null,null,"Soggetti",null,"Operations research"]
[{"$nulls":4},"Social sciences - Mathematics"]
[{"$nulls":4},"Mathematical models"]
[{"$nulls":4},"Operations Research and Decision Theory"]
[{"$nulls":4},"Mathematics in Business, Economics and Finance"]
[{"$nulls":4},"Mathematical Modeling and Industrial Mathematics"]
[null,null,"Lingua di pubblicazione",null,"Inglese"]
[null,null,"Formato",null,"Materiale a stampa"]
[null,null,null,"Livello bibliografico","Monografia"]
[null,null,null,"Nota di bibliografia","Includes bibliographical references."]
[null,null,"Nota di contenuto",null,"1. Introduction -- 2. The Portfolio Management Problem -- 3. Multicriteria Decision Analysis Methods -- 3. Literature Review -- 5. The Proposed Methodology -- 6. Information System in Python.-7. Empirical Testing -- 8. Conclusions -- Bibliography."]
[null,null,null,"Sommario/riassunto","This book covers topics in portfolio management and multicriteria decision analysis (MCDA), presenting a transparent and unified methodology for the portfolio construction process. The most important feature of the book includes the proposed methodological framework that integrates two individual subsystems, the portfolio selection subsystem and the portfolio optimization subsystem. An additional highlight of the book includes the detailed, step-by-step implementation of the proposed multicriteria algorithms in Python. The implementation is presented in detail; each step is elaborately described, from the input of the data to the extraction of the results. Algorithms are organized into small cells of code, accompanied by targeted remarks and comments, in order to help the reader to fully understand their mechanics. Readers are provided with a link to access the source code through GitHub. This Work may also be considered as a reference which presents the state-of-art research on portfolio construction with multiple and complex investment objectives and "]
[{"$nulls":4},"constraints. The book consists of eight chapters. A brief introduction is provided in Chapter 1. The fundamental issues of modern portfolio theory are discussed in Chapter 2. In Chapter 3, the various multicriteria decision aid methods, either discrete or continuous, are concisely described. In Chapter 4, a comprehensive review of the published literature in the field of multicriteria portfolio management is considered. In Chapter 5, an integrated and original multicriteria portfolio construction methodology is developed. Chapter 6 presents the web-based information system, in which the suggested methodological framework has been implemented. In Chapter 7, the experimental application of the proposed methodology is discussed and in Chapter 8, the authors provide overall conclusions. The readership of the book aims to be a diverse group, including fund managers, risk managers, investmentadvisors, bankers, private investors, analytics scientists, operations researchers scientists, and computer engineers, to name just several. Portions of the book may be used as instructional for either advanced undergraduate or post-graduate courses in investment analysis, portfolio engineering, decision science, computer science, or financial engineering."]
[null,"2.","Record Nr.",null,"UNINA9910953886203321"]
[null,null,"Autore",null,"Mitra Partha"]
[null,null,"Titolo",null,"Observed brain dynamics / / Partha P. Mitra, Hemant Bokil"]
[null,null,"Pubbl/distr/stampa",null,"Oxford ; ; New York, : Oxford University Press, c2008"]
[null,null,"ISBN",null,"0-19-988436-6"]
[{"$nulls":4},"0-19-803963-8"]
[{"$nulls":4},"9786611374624"]
[{"$nulls":4},"1-281-37462-8"]
[{"$nulls":4},"1-4356-3359-8"]
[null,null,"Edizione",null,"[1st ed.]"]
[null,null,"Descrizione fisica",null,"1 online resource (xxii, 381 pages) : illustrations"]
[null,null,"Disciplina",null,"612.8/2"]
[null,null,"Soggetti",null,"Neural conduction - Mathematical models"]
[{"$nulls":4},"Brain - Mathematical models"]
[{"$nulls":4},"Brain - Physiology"]
[{"$nulls":4},"Neural networks (Neurobiology)"]
[{"$nulls":4},"Electroencephalography"]
[{"$nulls":4},"Neural Conduction - physiology"]
[{"$nulls":4},"Models, Theoretical"]
[{"$nulls":4},"Brain - physiology"]
[null,null,"Lingua di pubblicazione",null,"Inglese"]
[null,null,"Formato",null,"Materiale a stampa"]
[null,null,null,"Livello bibliografico","Monografia"]
[null,null,null,"Note generali","Description based upon print version of record."]
[null,null,null,"Nota di bibliografia","Includes bibliographical references (p. 349-361) and index."]
[null,null,"Nota di contenuto",null,"Contents; PART I: Conceptual Background; 1 Why Study Brain Dynamics?; 1.1 Why Dynamics? An Active Perspective; 1.2 Quantifying Dynamics: Shared Theoretical Instruments; 1.3 ''Newtonian and Bergsonian Time''; 2 Theoretical Accounts of the Nervous System; 2.1 Three Axes in the Space of Theories; 3 Engineering Theories and Nervous System Function; 3.1 What Do Brains Do?; 3.2 Engineering Theories; 4 Methodological Considerations; 4.1 Conceptual Clarity and Valid Reasoning; 4.2 Nature of Scientific Method; PART II: Tutorials; 5 Mathematical Preliminaries; 5.1 Scalars: Real and Complex Variables"]
[{"$nulls":4},"Elementary Functions5.2 Vectors and Matrices: Linear Algebra; 5.3 Fourier Analysis; 5.4 Time Frequency Analysis; 5.5 Probability Theory; 5.6 Stochastic Processes; 6 Statistical Protocols; 6.1 Data Analysis Goals; 6.2 An Example of a Protocol: Method of Least Squares; 6.3 Classical and Modern Approaches; 6.4 Classical Approaches: Estimation and Inference; 7 Time Series Analysis; 7.1 Method of Moments; 7.2 "]
[{"$nulls":4},"Evoked Potentials and Peristimulus Time Histogram; 7.3 Univariate Spectral Analysis; 7.4 Bivariate Spectral Analysis; 7.5 Multivariate Spectral Analysis; 7.6 Prediction"]
[{"$nulls":4},"7.7 Point Process Spectral Estimation7.8 Higher Order Correlations; PART III: Applications; 8 Electrophysiology: Microelectrode Recordings; 8.1 Introduction; 8.2 Experimental Approaches; 8.3 Biophysics of Neurons; 8.4 Measurement Techniques; 8.5 Analysis Protocol; 8.6 Parametric Methods; 8.7 Predicting Behavior From Neural Activity; 9 Spike Sorting; 9.1 Introduction; 9.2 General Framework; 9.3 Data Acquisition; 9.4 Spike Detection; 9.5 Clustering; 9.6 Quality Metrics; 10 Electro- and Magnetoencephalography; 10.1 Introduction; 10.2 Analysis of Electroencephalographic Signals: Early Work"]
[{"$nulls":4},"10.3 Physics of Encephalographic Signals10.4 Measurement Techniques; 10.5 Analysis; 11 PET and fMRI; 11.1 Introduction; 11.2 Biophysics of PET and fMRI; 11.3 Experimental Overview; 11.4 Analysis; 12 Optical Imaging; 12.1 Introduction; 12.2 Biophysical Considerations; 12.3 Analysis; PART IV: Special Topics; 13 Local Regression and Likelihood; 13.1 Local Regression; 13.2 Local Likelihood; 13.3 Density Estimation; 13.4 Model Assessment and Selection; 14 Entropy and Mutual Information; 14.1 Entropy and Mutual Information for Discrete Random Variables; 14.2 Continuous Random Variables"]
[{"$nulls":4},"14.3 Discrete-Valued Discrete-Time Stochastic Processes14.4 Continuous-Valued Discrete-Time Stochastic Processes; 14.5 Point Processes; 14.6 Estimation Methods; Appendix A: The Bandwagon; Appendix B: Two Famous Papers; Photograph Credits; Bibliography; Index; A; B; C; D; E; F; G; H; I; J; K; L; M; N; O; P; Q; R; S; T; U; V; W; Y"]
[null,null,null,"Sommario/riassunto","The biomedical sciences have recently undergone revolutionary change, due to the ability to digitize and store large data sets. In neuroscience, the data sources include measurements of neural activity measured using electrode arrays, EEG and MEG, brain imaging data from PET, fMRI, and optical imaging methods. Analysis, visualization, and management of these time series data sets is a growing field of research that has become increasingly important both for experimentalists and theorists interested in brain function. Written by investigators who have played an important role in developing the"]