Information Geometry and Its Applications (Applied Mathematical Sciences, 194)

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Artikelmerkmale

Artikelzustand
Neu: Neues, ungelesenes, ungebrauchtes Buch in makellosem Zustand ohne fehlende oder beschädigte ...
is_adult_product
false
binding
hardcover
edition
1st ed. 2016
series_number
194
MPN
94 black & white illustrations, biograph
manufacturer
Springer
series_title
Applied Mathematical Sciences, 194
pages
387
genre
Differential & Riemannian geometry
publication_date
2016-02-10T00:00:01Z
unspsc_code
55101500
subject_code
PBMP
gpsr_safety_attestation
true
target_audience
Professional and scholarly
edition_number
1
batteries_required
false
volume
194
Brand
Springer
number_of_items
1
ghs
[object Object]
part_number
94 black & white illustrations, biograph
batteries_included
false
ISBN
9784431559771
Kategorie

Über dieses Produkt

Product Identifiers

Publisher
Springer Japan
ISBN-10
4431559779
ISBN-13
9784431559771
eBay Product ID (ePID)
219083481

Product Key Features

Number of Pages
Xiii, 374 Pages
Publication Name
Information Geometry and Its Applications
Language
English
Subject
Geometry / Differential, Probability & Statistics / General, Applied
Publication Year
2016
Type
Textbook
Author
Shun-Ichi Amari
Subject Area
Mathematics
Series
Applied Mathematical Sciences Ser.
Format
Hardcover

Dimensions

Item Weight
248.2 Oz
Item Length
9.3 in
Item Width
6.1 in

Additional Product Features

Intended Audience
Scholarly & Professional
LCCN
2015-958849
Reviews
"This book gives a reasonably accessible introduction to the subject and then considers various applications. ... The book provides a nice introduction to the subject. ... the book provides a nice introduction to a difficult subject that has many important applications." (Marvin H. J. Gruber, Technometrics, Vol. 58 (4), April, 2016)
Dewey Edition
23
Series Volume Number
194
Number of Volumes
1 vol.
Illustrated
Yes
Dewey Decimal
516.3/6
Table Of Content
1 Manifold, Divergence and Dually Flat Structure.- 2 Exponential Families and Mixture Families of Probability.- 3 Invariant Geometry of Manifold of Probability.- 4 α-Geometry, Tsallis q-Entropy and Positive-Definite.- 5 Elements of Differential Geometry.- 6 Dual Affine Connections and Dually Flat Manifold.- 7 Asymptotic Theory of Statistical Inference.- 8 Estimation in the Presence of Hidden Variables.- 9 Neyman-Scott Problem.- 10 Linear Systems and Time Series.- 11 Machine Learning.- 12 Natural Gradient Learning and its Dynamics in Singular.- 13 Signal Processing and Optimization.- Index.
Synopsis
This is the first comprehensive book on information geometry, written by the founder of the field. It begins with an elementary introduction to dualistic geometry and proceeds to a wide range of applications, covering information science, engineering, and neuroscience. It consists of four parts, which on the whole can be read independently. A manifold with a divergence function is first introduced, leading directly to dualistic structure, the heart of information geometry. This part (Part I) can be apprehended without any knowledge of differential geometry. An intuitive explanation of modern differential geometry then follows in Part II, although the book is for the most part understandable without modern differential geometry. Information geometry of statistical inference, including time series analysis and semiparametric estimation (the Neyman-Scott problem), is demonstrated concisely in Part III. Applications addressed in Part IV include hot current topics in machine learning, signal processing, optimization, and neural networks. The book is interdisciplinary, connecting mathematics, information sciences, physics, and neurosciences, inviting readers to a new world of information and geometry. This book is highly recommended to graduate students and researchers who seek new mathematical methods and tools useful in their own fields., This is the first comprehensive book on information geometry, written by the founder of the field. It begins with an elementary introduction to dualistic geometry and proceeds to a wide range of applications, covering information science, engineering, and neuroscience. It consists of four parts, which on the whole can be read independently. A manifold with a divergence function is first introduced, leading directly to dualistic structure, the heart of information geometry. This part (Part I) can be apprehended without any knowledge of differential geometry. An intuitive explanation of modern differential geometry then follows in Part II, although the book is for the most part understandable without modern differential geometry. Information geometry of statistical inference, including time series analysis and semiparametric estimation (the Neyman-Scott problem), is demonstrated concisely in Part III. Applications addressed in Part IV include hot current topics in machine learning,signal processing, optimization, and neural networks. The book is interdisciplinary, connecting mathematics, information sciences, physics, and neurosciences, inviting readers to a new world of information and geometry. This book is highly recommended to graduate students and researchers who seek new mathematical methods and tools useful in their own fields.
LC Classification Number
QA641-670

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