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Statistical physics of data assimilation and machine learning, Abarbanel, Henry D. I. (university Of California, San Diego)


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Автор: Abarbanel, Henry D. I. (university Of California, San Diego)
Название:  Statistical physics of data assimilation and machine learning
ISBN: 9781316519639
Издательство: Cambridge Academ
Классификация:


ISBN-10: 1316519635
Обложка/Формат: Hardback
Страницы: 204
Вес: 0.53 кг.
Дата издания: 17.02.2022
Серия: Physics
Язык: English
Издание: New ed
Иллюстрации: Worked examples or exercises
Размер: 158 x 236 x 27
Читательская аудитория: Tertiary education (us: college)
Ключевые слова: Data capture & analysis,Machine learning,Statistical physics, SCIENCE / Physics / Mathematical & Computational
Ссылка на Издательство: Link
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Поставляется из: Англии
Описание: The theory of data assimilation and machine learning is introduced in an accessible and pedagogical manner, with a focus on the underlying statistical physics. This modern and cross-disciplinary book is suitable for undergraduate and graduate students from science and engineering without specialized experience of statistical physics.


Cellular Biophysics and Modeling: A Primer on the Computational Biology of Excitable Cells

Автор: Greg Conradi Smith
Название: Cellular Biophysics and Modeling: A Primer on the Computational Biology of Excitable Cells
ISBN: 1107005361 ISBN-13(EAN): 9781107005365
Издательство: Cambridge Academ
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Цена: 15046.00 р.
Наличие на складе: Поставка под заказ.

Описание: What every neuroscientist should know about the mathematical modeling of excitable cells. Combining empirical physiology and nonlinear dynamics, this text provides an introduction to the simulation and modeling of dynamic phenomena in cell biology and neuroscience. It introduces mathematical modeling techniques alongside cellular electrophysiology. Topics include membrane transport and diffusion, the biophysics of excitable membranes, the gating of voltage and ligand-gated ion channels, intracellular calcium signalling, and electrical bursting in neurons and other excitable cell types. It introduces mathematical modeling techniques such as ordinary differential equations, phase plane, and bifurcation analysis of single-compartment neuron models. With analytical and computational problem sets, this book is suitable for life sciences majors, in biology to neuroscience, with one year of calculus, as well as graduate students looking for a primer on membrane excitability and calcium signalling.

First Course in Network Science

Автор: Menczer Filippo
Название: First Course in Network Science
ISBN: 1108471137 ISBN-13(EAN): 9781108471138
Издательство: Cambridge Academ
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Цена: 6494.00 р.
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Описание: A practical introduction to network science suitable for students studying diverse programs such as business, cognitive science, neuroscience, sociology, biology, and engineering. A wide range of examples and exercises develop readers` understanding, and Python programming tutorials provided online reinforce coding skills.

Cambridge Series in Statistical and Probabilistic Mathematic

Автор: Wainwright Martin J
Название: Cambridge Series in Statistical and Probabilistic Mathematic
ISBN: 1108498027 ISBN-13(EAN): 9781108498029
Издательство: Cambridge Academ
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Цена: 10771.00 р.
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Описание: Recent years have seen an explosion in the volume and variety of data collected in scientific disciplines from astronomy to genetics and industrial settings ranging from Amazon to Uber. This graduate text equips readers in statistics, machine learning, and related fields to understand, apply, and adapt modern methods suited to large-scale data.

Noise Sensitivity of Boolean Functions and Percolation

Автор: Garban
Название: Noise Sensitivity of Boolean Functions and Percolation
ISBN: 1107432553 ISBN-13(EAN): 9781107432550
Издательство: Cambridge Academ
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Цена: 6019.00 р.
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Описание: This account of the new and exciting area of noise sensitivity of Boolean functions - in particular applied to critical percolation - is designed for graduate students and researchers in probability theory, discrete mathematics, and theoretical computer science. It assumes a basic background in probability theory and integration theory. Each chapter ends with exercises.

Statistical Reinforcement Learning

Автор: Sugiyama
Название: Statistical Reinforcement Learning
ISBN: 1439856893 ISBN-13(EAN): 9781439856895
Издательство: Taylor&Francis
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Цена: 13779.00 р.
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Описание:

Reinforcement learning is a mathematical framework for developing computer agents that can learn an optimal behavior by relating generic reward signals with its past actions. With numerous successful applications in business intelligence, plant control, and gaming, the RL framework is ideal for decision making in unknown environments with large amounts of data.

Supplying an up-to-date and accessible introduction to the field, Statistical Reinforcement Learning: Modern Machine Learning Approaches presents fundamental concepts and practical algorithms of statistical reinforcement learning from the modern machine learning viewpoint. It covers various types of RL approaches, including model-based and model-free approaches, policy iteration, and policy search methods.

  • Covers the range of reinforcement learning algorithms from a modern perspective
  • Lays out the associated optimization problems for each reinforcement learning scenario covered
  • Provides thought-provoking statistical treatment of reinforcement learning algorithms

The book covers approaches recently introduced in the data mining and machine learning fields to provide a systematic bridge between RL and data mining/machine learning researchers. It presents state-of-the-art results, including dimensionality reduction in RL and risk-sensitive RL. Numerous illustrative examples are included to help readers understand the intuition and usefulness of reinforcement learning techniques.

This book is an ideal resource for graduate-level students in computer science and applied statistics programs, as well as researchers and engineers in related fields.

Network Science

Автор: Barab?si
Название: Network Science
ISBN: 1107076269 ISBN-13(EAN): 9781107076266
Издательство: Cambridge Academ
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Цена: 7762.00 р.
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Описание: Illustrated throughout in full colour, this pioneering textbook, spanning a wide range of disciplines from physics to the social sciences, is the only book needed for an introduction to network science. In modular format, with clear delineation between undergraduate and graduate material, its unique design is supported by extensive online resources.

Data-Driven Computational Neuroscience: Machine Learning and Statistical Models

Автор: Concha Bielza, Pedro Larranaga
Название: Data-Driven Computational Neuroscience: Machine Learning and Statistical Models
ISBN: 110849370X ISBN-13(EAN): 9781108493703
Издательство: Cambridge Academ
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Цена: 12830.00 р.
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Описание: Data-driven computational neuroscience facilitates the transformation of data into insights into the structure and functions of the brain. This modern treatment of real world cases offers neuroscience researchers and graduate students a comprehensive, in-depth guide to statistical and machine learning methods.

Cambridge series in statistical and probabilistic mathematics

Автор: Bouveyron, Charles Celeux, Gilles Murphy, T. Brendan (university College Dublin) Raftery, Adrian E. (university Of Washington)
Название: Cambridge series in statistical and probabilistic mathematics
ISBN: 110849420X ISBN-13(EAN): 9781108494205
Издательство: Cambridge Academ
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Цена: 11563.00 р.
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Описание: This accessible but rigorous introduction is written for advanced undergraduates and beginning graduate students in data science, as well as researchers and practitioners. It shows how a statistical framework yields sound estimation, testing and prediction methods, using extensive data examples and providing R code for many methods.

Spectral Analysis for Univariate Time Series

Автор: Donald B. Percival, Andrew T. Walden
Название: Spectral Analysis for Univariate Time Series
ISBN: 1107028140 ISBN-13(EAN): 9781107028142
Издательство: Cambridge Academ
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Цена: 14573.00 р.
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Описание: Spectral analysis is an important technique for interpreting time series data. This book uses the R language and real world examples to show data analysts interested in time series in the environmental, engineering and physical sciences how to bridge the gap between the statistical theory behind spectral analysis and its application to actual data.

Statistical mechanics of liquids and solutions :

Автор: Kjellander, Roland,
Название: Statistical mechanics of liquids and solutions :
ISBN: 1482244012 ISBN-13(EAN): 9781482244014
Издательство: Taylor&Francis
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Цена: 19140.00 р.
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Описание: This book shows how you can start from basic laws for the interactions and motions of microscopic particles and calculate how macroscopic systems of these particles behave, thereby explaining properties of matter at the scale that we perceive.

A Modern Course in Statistical Physics

Автор: Reichl Linda E.
Название: A Modern Course in Statistical Physics
ISBN: 3527413499 ISBN-13(EAN): 9783527413492
Издательство: Wiley
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Цена: 14256.00 р.
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Описание: Presents essential concepts from equilibrium and non-equlibrium statistical physics at a level accessible to advanced undergraduate and graduate students. This edition includes latest methods of quantum statistical mechanics and modern aspects of turbulent hydrodynamic flow.

An Introduction to Thermodynamics and Statistical Mechanics

Автор: Stowe
Название: An Introduction to Thermodynamics and Statistical Mechanics
ISBN: 1107694922 ISBN-13(EAN): 9781107694927
Издательство: Cambridge Academ
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Цена: 8554.00 р.
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Описание: An introductory textbook for standard undergraduate courses in thermodynamics, covering important quantum behaviours, classical thermodynamics and statistical mechanics.


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