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158367166
Probabilistic Numerics: Computation as Machine Learning, (Hardcover)
92% of respondents would recommend this to a friend
Item #:
158367166
GMD 6649
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What Stands Out
Integrative Approach
Combines traditional numerical methods with machine learning, providing a new framework for solving computational problems more efficiently than conventional techniques.
Advanced Techniques
Incorporates cutting-edge probabilistic methods, enabling enhanced accuracy and adaptability in diverse applications, addressing limitations faced by traditional algorithms.
Interdisciplinary Appeal
Designed for researchers and practitioners across mathematics, computer science, and engineering, fostering collaboration and innovation in the fields of computational and statistical learning.
Product Details
- Probabilistic numerical computation formalises the connection between machine learning and applied mathematics. Numerical algorithms approximate intractable quantities from computable ones. They estimate integrals from evaluations of the integrand, or the path of a dynamical system described by differential equations from evaluations of the vector field. In other words, they infer a latent quantity from data. This book shows that it is thus formally possible to think of computational routines as learning machines, and to use the notion of Bayesian inference to build more flexible, efficient, or customised algorithms for computation. The text caters for Masters' and PhD students, as well as postgraduate researchers in artificial intelligence, computer science, statistics, and applied mathematics. Extensive background material is provided along with a wealth of figures, worked examples, and exercises (with solutions) to develop intuition.
| Manual & guide type | Instruction Manual |
| Book format | Hardcover |
| Edition | 1 |
| Skill level | Advanced |
| Pages | 410 |
| Language | English |
| Brand | Philipp Hennig |
| Pub date | 2022-06-30 |
| Author | Cambridge University Press |
| Subgenre | General |
| Genre | Computing & Internet |
| Bisac subject heading | Computers |
| Series title | No Series |
| Dimensions | 8.03 x 0.94 x 10.00 in (20.4 x 2.4 x 25.4 cm) |
| Age group | Adult |
| Retail packaging | Single Piece |
| Original languages | English |
Who Should Buy?
-
Mathematics Researchers
Ideal for researchers exploring uncertainty quantification and the integration of machine learning into computational mathematics.
-
Data Scientists
Beneficial for data scientists seeking to enhance predictive modeling through probabilistic approaches in numerical computation.
-
Graduate Students
Recommended for graduate students studying numerical methods and machine learning who require advanced theoretical insights.
-
Casual Readers
Not suitable for casual readers lacking a strong background in mathematics or computational sciences.
Product Description
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Philipp Hennig General Computers & Technology Books Editorial Review
Probabilistic Numerics: Computation as Machine Learning is an advanced hardcover book that delves into the intersection of probabilistic methods and computational mathematics, providing a thorough instructional manual for readers with a strong background in the field. Authored by Philipp Hennig and published by Cambridge University Press, this 410-page volume explores foundational concepts and cutting-edge techniques, making it a valuable resource for academics and professionals alike. The book is designed for adults and is the first edition, focusing on the growing discipline of probabilistic numerics, and is highlighted for its clear explanations and comprehensive approach to complex topics.
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Pros
- Comprehensive coverage of probabilistic methods
- Designed for advanced readers
- High-quality hardcover format
- Well-organized instructional manual
- Published by Cambridge University Press
Cons
- Content may be too advanced for beginners
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