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Large language models, Atkinson-abutridy, John


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Цена: 7501.00р.
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Автор: Atkinson-abutridy, John
Название:  Large language models
ISBN: 9781032836089
Издательство: Taylor&Francis
Классификация:










ISBN-10: 1032836083
Обложка/Формат: Paperback
Страницы: 168
Вес: 0.35 кг.
Дата издания: 17.10.2024
Иллюстрации: 1 tables, color; 16 tables, black and white; 69 line drawings, color; 9 line drawings, black and white; 3 halftones, color; 72 illustrations, color; 9 illustrations, black and white
Размер: 253 x 179 x 15
Подзаголовок: Concepts, techniques and applications
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Поставляется из: Европейский союз


Causality

Автор: Pearl, Judea
Название: Causality
ISBN: 052189560X ISBN-13(EAN): 9780521895606
Издательство: Cambridge Academ
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Цена: 9029.00 р.
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Описание: Written by one of the preeminent researchers in the field, this book provides a comprehensive exposition of modern analysis of causation. It shows how causality has grown from a nebulous concept into a mathematical theory with significant applications in the fields of statistics, artificial intelligence, philosophy, and cognitive science.

Quantitative Trading

Автор: Guo, Xin , Lai, Tze Leung , Shek, Howard , Wong
Название: Quantitative Trading
ISBN: 0367871815 ISBN-13(EAN): 9780367871819
Издательство: Taylor&Francis
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Цена: 10104.00 р.
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Описание: The first part of this book discusses institutions and mechanisms of algorithmic trading, market microstructure, high-frequency data and stylized facts, time and event aggregation, order book dynamics, trading strategies and algorithms, transaction costs, market impact and execution strategies, risk analysis, and management. The second part cove

Applications of Linear and Nonlinear Models

Автор: Erik W. Grafarend , Silvelyn Zwanzig , Joseph L. Awange
Название: Applications of Linear and Nonlinear Models
ISBN: 3030945979 ISBN-13(EAN): 9783030945978
Издательство: Springer
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Цена: 24392.00 р.
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Описание: This book provides numerous examples of linear and nonlinear model applications. Here, we present a nearly complete treatment of the Grand Universe of linear and weakly nonlinear regression models within the first 8 chapters. Our point of view is both an algebraic view and a stochastic one. For example, there is an equivalent lemma between a best, linear uniformly unbiased estimation (BLUUE) in a Gauss–Markov model and a least squares solution (LESS) in a system of linear equations. While BLUUE is a stochastic regression model, LESS is an algebraic solution. In the first six chapters, we concentrate on underdetermined and overdetermined linear systems as well as systems with a datum defect. We review estimators/algebraic solutions of type MINOLESS, BLIMBE, BLUMBE, BLUUE, BIQUE, BLE, BIQUE, and total least squares. The highlight is the simultaneous determination of the first moment and the second central moment of a probability distribution in an inhomogeneous multilinear estimation by the so-called E-D correspondence as well as its Bayes design. In addition, we discuss continuous networks versus discrete networks, use of Grassmann–Plucker coordinates, criterion matrices of type Taylor–Karman as well as FUZZY sets. Chapter seven is a speciality in the treatment of an overjet. This second edition adds three new chapters: (1) Chapter on integer least squares that covers (i) model for positioning as a mixed integer linear model which includes integer parameters. (ii) The general integer least squares problem is formulated, and the optimality of the least squares solution is shown. (iii) The relation to the closest vector problem is considered, and the notion of reduced lattice basis is introduced. (iv) The famous LLL algorithm for generating a Lovasz reduced basis is explained. (2) Bayes methods that covers (i) general principle of Bayesian modeling. Explain the notion of prior distribution and posterior distribution. Choose the pragmatic approach for exploring the advantages of iterative Bayesian calculations and hierarchical modeling. (ii) Present the Bayes methods for linear models with normal distributed errors, including noninformative priors, conjugate priors, normal gamma distributions and (iii) short outview to modern application of Bayesian modeling. Useful in case of nonlinear models or linear models with no normal distribution: Monte Carlo (MC), Markov chain Monte Carlo (MCMC), approximative Bayesian computation (ABC) methods. (3) Error-in-variables models, which cover: (i) Introduce the error-in-variables (EIV) model, discuss the difference to least squares estimators (LSE), (ii) calculate the total least squares (TLS) estimator. Summarize the properties of TLS, (iii) explain the idea of simulation extrapolation (SIMEX) estimators, (iv) introduce the symmetrized SIMEX (SYMEX) estimator and its relation to TLS, and (v) short outview to nonlinear EIV models. The chapter on algebraic solution of nonlinear system of equations has also been updated in line with the new emerging field of hybrid numeric-symbolic solutions to systems of nonlinear equations, ermined system of nonlinear equations on curved manifolds. The von Mises–Fisher distribution is characteristic for circular or (hyper) spherical data. Our last chapter is devoted to probabilistic regression, the special Gauss–Markov model with random effects leading to estimators of type BLIP and VIP including Bayesian estimation. A great part of the work is presented in four appendices. Appendix A is a treatment, of tensor algebra, namely linear algebra, matrix algebra, and multilinear algebra. Appendix B is devoted to sampling distributions and their use in terms of confidence intervals and confidence regions. Appendix C reviews the elementary notions of statistics, namely random events and stochastic processes. Appendix D introduces the basics of Groebner basis algebra, its careful definition, the Buchberger algorithm, especially the C. F. Gauss combinatorial algorithm.

Analytically Tractable Stochastic Stock Price Models

Автор: Gulisashvili
Название: Analytically Tractable Stochastic Stock Price Models
ISBN: 3642312136 ISBN-13(EAN): 9783642312137
Издательство: Springer
Цена: 4866.00 р. 6951.00 -30%
Наличие на складе: Есть (1 шт.)
Описание: For instance, in the Hull-White model the volatility process is a geometric Brownian motion, the Stein-Stein model uses an Ornstein-Uhlenbeck process as the stochastic volatility, and in the Heston model a Cox-Ingersoll-Ross process governs the behavior of the volatility.

Introduction to mathematical models in operations planning

Автор: Tayali, Halit Alper (istanbul Universitesi)
Название: Introduction to mathematical models in operations planning
ISBN: 1032191996 ISBN-13(EAN): 9781032191997
Издательство: Taylor&Francis
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Цена: 8879.00 р.
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Introduction to dynamic systems :

Автор: Luenberger David G.
Название: Introduction to dynamic systems :
ISBN: 0471025941 ISBN-13(EAN): 9780471025948
Издательство: Wiley
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Цена: 28601.00 р. 40859.00 -30%
Наличие на складе: Есть (1 шт.)
Описание: Integrates the traditional approach to differential equations with the modern systems and control theoretic approach to dynamic systems, emphasizing theoretical principles and classic models in a wide variety of areas.

Автор: Anand Sharma
Название: Knowledge Engineering for Modern Information Systems
ISBN: 3110713160 ISBN-13(EAN): 9783110713169
Издательство: Walter de Gruyter
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Цена: 23049.00 р.
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Описание:

Knowledge Engineering (KE) is a fi eld within artifi cial intelligence that develops knowledgebased systems. KE is the process of imitating how a human expert in a specifi c domain would act and take decisions. It contains large amounts of knowledge, like metadata and information about a data object that describes characteristics such as content, quality, and format, structure and processes. Such systems are computer programs that are the basis of how a decision is made or a conclusion is reached. It is having all the rules and reasoning mechanisms to provide solutions to real-world problems. This book presents an extensive collection of the recent fi ndings and innovative research in the information system and KE domain. Highlighting the challenges and diffi culties in implementing these approaches, this book is a critical reference source for academicians, professionals, engineers, technology designers, analysts, undergraduate and postgraduate students in computing science and related disciplines such as Information systems, Knowledge Engineering, Intelligent Systems, Artifi cial Intelligence, Cognitive Neuro - science, and Robotics. In addition, anyone who is interested or involved in sophisticated information systems and knowledge engineering developments will fi nd this book a valuable source of ideas and guidance.

Media management and artificial intelligence

Автор: Connock, Alex
Название: Media management and artificial intelligence
ISBN: 103210094X ISBN-13(EAN): 9781032100944
Издательство: Taylor&Francis
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Цена: 6123.00 р.
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Описание: This cutting-edge textbook examines contemporary media business models in the context of Artificial Intelligence and digital transformation. AI has dramatically impacted media production and distribution, from recommendation engines to synthetic humans, from video-to-text tools to natural language models.

Regression: Models, Methods and Applications

Автор: Fahrmeir Ludwig, Kneib Thomas, Lang Stefan
Название: Regression: Models, Methods and Applications
ISBN: 3662638819 ISBN-13(EAN): 9783662638811
Издательство: Springer
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Цена: 13415.00 р.
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Описание: Now in its second edition, this textbook provides an applied and unified introduction to parametric, nonparametric and semiparametric regression that closes the gap between theory and application. The most important models and methods in regression are presented on a solid formal basis, and their appropriate application is shown through numerous examples and case studies. The most important definitions and statements are concisely summarized in boxes, and the underlying data sets and code are available online on the book’s dedicated website. Availability of (user-friendly) software has been a major criterion for the methods selected and presented. The chapters address the classical linear model and its extensions, generalized linear models, categorical regression models, mixed models, nonparametric regression, structured additive regression, quantile regression and distributional regression models. Two appendices describe the required matrix algebra, as well as elements of probability calculus and statistical inference. In this substantially revised and updated new edition the overview on regression models has been extended, and now includes the relation between regression models and machine learning, additional details on statistical inference in structured additive regression models have been added and a completely reworked chapter augments the presentation of quantile regression with a comprehensive introduction to distributional regression models. Regularization approaches are now more extensively discussed in most chapters of the book. The book primarily targets an audience that includes students, teachers and practitioners in social, economic, and life sciences, as well as students and teachers in statistics programs, and mathematicians and computer scientists with interests in statistical modeling and data analysis. It is written at an intermediate mathematical level and assumes only knowledge of basic probability, calculus, matrix algebra and statistics.

Artificial economics :

Автор: Mercado, P. Ruben,
Название: Artificial economics :
ISBN: 1009005758 ISBN-13(EAN): 9781009005753
Издательство: Cambridge Academ
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Цена: 5069.00 р.
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Описание: An introductory overview of the methods, models and interdisciplinary links of artificial economics. Addresses the differences between the assumptions and methods of artificial economics and those of mainstream economics. This is one of the first books to fully address, in an intuitive and conceptual form, this new way of doing economics.

Handbook of Measurement Error Models

Автор: Grace Y. Yi, Aurore Delaigle, Paul Gustafson
Название: Handbook of Measurement Error Models
ISBN: 1138106402 ISBN-13(EAN): 9781138106406
Издательство: Taylor&Francis
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Цена: 35218.00 р.
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Описание: Reference text for statistical methods and applications for measurement error models for: researchers who work with error-contaminated data, graduate students from statistics and biostatistics, analysts in multiple fields, including medical research, biosciences, nutritional studies, epidemiological studies and environmental studies.

Mathematical models for decision making with multiple perspectives :

Автор: Gomes, Maria Isabel,
Название: Mathematical models for decision making with multiple perspectives :
ISBN: 0367440741 ISBN-13(EAN): 9780367440749
Издательство: Taylor&Francis
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Цена: 24499.00 р.
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Описание: This book brings together, in a single volume, the fields of multicriteria decision making and multiobjective optimization that are traditionally covered by different books. It is written in a didactic form using examples to help understanding of the proposed methodologies better.


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