Effective Statistical Learning Methods for Actuaries III : Neural Networks and Extensions
(2019) ISBN: [9783030258269], 250 pages, published
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- Abstract
- Artificial intelligence and neural networks offer a powerful alternative to statistical methods for analyzing data. This book reviews some of the most recent developments in neural networks, with a focus on applications in actuarial sciences and finance. The third volume of the trilogy simultaneously introduces the relevant tools for developing and analyzing neural networks, in a style that is mathematically rigorous and yet accessible. The authors proceed by successive generalizations, requiring of the reader only a basic knowledge of statistics. 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. This book is written for masters students in the actuarial sciences and for actuaries wishing to update their skills in machine learning.
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Denuit, M., Hainaut, D., & Trufin, J. (2019). Effective Statistical Learning Methods for Actuaries III : Neural Networks and Extensions (Springer Actuarial). Springer. https://doi.org/10.1007/978-3-030-25827-6