Probabilistic Modeling in Bioinformatics and Medical Informatics, Dirk Husmeier; Richard Dybowski; Stephen Roberts
Автор: Koller Daphne, Friedman Nir Название: Probabilistic Graphical Models: Principles and Techniques ISBN: 0262013193 ISBN-13(EAN): 9780262013192 Издательство: MIT Press Рейтинг: Цена: 21161.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание:
A general framework for constructing and using probabilistic models of complex systems that would enable a computer to use available information for making decisions.
Most tasks require a person or an automated system to reason -- to reach conclusions based on available information. The framework of probabilistic graphical models, presented in this book, provides a general approach for this task. The approach is model-based, allowing interpretable models to be constructed and then manipulated by reasoning algorithms. These models can also be learned automatically from data, allowing the approach to be used in cases where manually constructing a model is difficult or even impossible. Because uncertainty is an inescapable aspect of most real-world applications, the book focuses on probabilistic models, which make the uncertainty explicit and provide models that are more faithful to reality.
Probabilistic Graphical Models discusses a variety of models, spanning Bayesian networks, undirected Markov networks, discrete and continuous models, and extensions to deal with dynamical systems and relational data. For each class of models, the text describes the three fundamental cornerstones: representation, inference, and learning, presenting both basic concepts and advanced techniques. Finally, the book considers the use of the proposed framework for causal reasoning and decision making under uncertainty. The main text in each chapter provides the detailed technical development of the key ideas. Most chapters also include boxes with additional material: skill boxes, which describe techniques; case study boxes, which discuss empirical cases related to the approach described in the text, including applications in computer vision, robotics, natural language understanding, and computational biology; and concept boxes, which present significant concepts drawn from the material in the chapter. Instructors (and readers) can group chapters in various combinations, from core topics to more technically advanced material, to suit their particular needs.
Автор: Bass Название: Probabilistic Techniques in Analysis ISBN: 0387943870 ISBN-13(EAN): 9780387943879 Издательство: Springer Рейтинг: Цена: 12012.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: Exploring the use of techniques drawn from probability research to tackle problems in mathematical analysis, this study includes discussion of the construction of the Martin boundary, Dahlberg`s Theorem, probabilistic proofs of the boundary Harnack principle, and much more.
Автор: Pardoux Название: Probabilistic Models of Population Evolution ISBN: 3319303260 ISBN-13(EAN): 9783319303260 Издательство: Springer Рейтинг: Цена: 4611.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: This expository book presents the mathematical description of evolutionary models of populations subject to interactions (e.g. competition) within the population. The author includes both models of finite populations, and limiting models as the size of the population tends to infinity. The size of the population is described as a random function of time and of the initial population (the ancestors at time 0). The genealogical tree of such a population is given. Most models imply that the population is bound to go extinct in finite time. It is explained when the interaction is strong enough so that the extinction time remains finite, when the ancestral population at time 0 goes to infinity. The material could be used for teaching stochastic processes, together with their applications.
?tienne Pardoux is Professor at Aix-Marseille University, working in the field of Stochastic Analysis, stochastic partial differential equations, and probabilistic models in evolutionary biology and population genetics. He obtained his PhD in 1975 at University of Paris-Sud.
Автор: Alon Noga Название: Probabilistic Method ISBN: 1119061954 ISBN-13(EAN): 9781119061953 Издательство: Wiley Рейтинг: Цена: 15357.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: Praise for the Third Edition Researchers of any kind of extremal combinatorics or theoretical computer science will welcome the new edition of this book.
Автор: Ben Goertzel; Matthew Ikl?; Izabela Freire Goertze Название: Probabilistic Logic Networks ISBN: 0387768718 ISBN-13(EAN): 9780387768717 Издательство: Springer Рейтинг: Цена: 21661.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: Describes Probabilistic Logic Networks (PLN), a novel conceptual, mathematical and computational approach to uncertain inference. This book reviews the conceptual and mathematical foundations of PLN, giving the specific algebra involved in each type of inference encompassed within PLN.
Описание: On various examples ranging from geosciences to environmental sciences, thisbook explains how to generate an adequate description of uncertainty, how to justifysemiheuristic algorithms for processing uncertainty, and how to make these algorithmsmore computationally efficient.
Автор: Narens Louis Название: Probabilistic Lattices: With Applications To Psychology ISBN: 9814630411 ISBN-13(EAN): 9789814630412 Издательство: World Scientific Publishing Рейтинг: Цена: 12514.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание:
There are many books on lattice theory in the field, but none interfaces with the foundations of probability. This book does. It also develops new probability theories with rigorous foundations for decision theory and applies them to specific well-known problematic examples. There is only one other book that attempts this. It uses quantum probability theory from physics. The new probability theories developed in this book are different; they are not borrowed from physics but are explicitly designed for decision theory.
Автор: J?zsef Beck Название: Probabilistic Diophantine Approximation ISBN: 3319354655 ISBN-13(EAN): 9783319354651 Издательство: Springer Рейтинг: Цена: 13275.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: This book gives a comprehensive treatment of random phenomena and distribution results in diophantine approximation, with a particular emphasis on quadratic irrationals.
Автор: Cooper, George R.; McGillem, Clare D. Название: Probabilistic Methods of Signal and System Analysis ISBN: 0195123549 ISBN-13(EAN): 9780195123548 Издательство: Oxford Academ Рейтинг: Цена: 36430.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: Originally published in 1971, this text is intended for signals and systems courses which emphasize probability. It provides an introduction to probability theory, statistics, random processes and the analysis of systems with random inputs. This edition has been updated and uses Matlab.
Автор: Zoltan Kovacs Название: Probabilistic Safety Assessment of WWER440 Reactors ISBN: 3319360701 ISBN-13(EAN): 9783319360706 Издательство: Springer Рейтинг: Цена: 14365.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: Introduction.- The nuclear power plant with WWER440 reactors.- Level 1 full power PSA.- Level 1 low power and shutdown PSA.- Level 2 PSA.- PSA applications.- Conclusions.
Описание: This text gives an interesting and useful blend of the mathematical, probabilistic and statistical tools used in heavy-tail analysis. It is uniquely devoted to heavy-tails and emphasizes both probability modeling and statistical methods for fitting models.
Автор: Reich Название: Probabilistic Forecasting and Bayesian Data Assimilation ISBN: 1107069394 ISBN-13(EAN): 9781107069398 Издательство: Cambridge Academ Рейтинг: Цена: 19325.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: This book focuses on the Bayesian approach to data assimilation, outlining the subject`s key ideas and concepts, and explaining how to implement specific data assimilation algorithms. It is an ideal introduction for graduate students in applied mathematics, computer science, engineering, geoscience and other emerging application areas.
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