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Effective Statistical Learning Methods for Actuaries III

Neural Networks and Extensions

Language EnglishEnglish
Book Paperback
Book Effective Statistical Learning Methods for Actuaries III Michel Denuit
Libristo code: 24519042
Publishers Springer, Berlin, November 2019
This book reviews some of the most recent developments in neural networks, with a focus on applicati... Full description
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This book reviews some of the most recent developments in neural networks, with a focus on applications in actuarial sciences and finance. It simultaneously introduces the relevant tools for developing and analyzing neural networks, in a style that is mathematically rigorous yet accessible. Artificial intelligence and neural networks offer a powerful alternative to statistical methods for analyzing data. Various topics are covered from feed-forward networks to deep learning, such as Bayesian learning, boosting methods and Long Short Term Memory models. All methods are applied to claims, mortality or time-series forecasting. Requiring only a basic knowledge of statistics, this book is written for masters students in the actuarial sciences and for actuaries wishing to update their skills in machine learning. This is the third of three volumes entitled Effective Statistical Learning Methods for Actuaries . Written by actuaries for actuaries, this series offers a comprehensive overview of insurance data analytics with applications to P&C, life and health insurance. Although closely related to the other two volumes, this volume can be read independently.

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About the book

Full name Effective Statistical Learning Methods for Actuaries III
Language English
Binding Book - Paperback
Date of issue 2019
Number of pages 250
EAN 9783030258269
Libristo code 24519042
Publishers Springer, Berlin
Weight 409
Dimensions 155 x 235 x 15
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