Statistical Foundations of Actuarial Learning and its Applications
Book Details
| Authors | Mario V. Wüthrich, Michael Merz |
| Publisher | Springer |
| Published | 2023 |
| Edition | 1st |
| Paperback | 605 pages |
| Language | English |
| ISBN-13 | 9783031124082, 9783031124112, 9783031124099 |
| ISBN-10 | 3031124081, 3031124111, 303112409X |
| License | Creative Commons Attribution |
Book Description
This open access book discusses the statistical modeling of insurance problems, a process which comprises data collection, data analysis and statistical model building to forecast insured events that may happen in the future. It presents the mathematical foundations behind these fundamental statistical concepts and how they can be applied in daily actuarial practice.
Statistical modeling has a wide range of applications, and, depending on the application, the theoretical aspects may be weighted differently: here the main focus is on prediction rather than explanation. Starting with a presentation of state-of-the-art actuarial models, such as generalized linear models, the book then dives into modern machine learning tools such as neural networks and text recognition to improve predictive modeling with complex features.
Providing practitioners with detailed guidance on how to apply machine learning methods to real-world data sets, and how to interpret the results without losing sight of the mathematical assumptions on which these methods are based, the book can serve as a modern basis for an actuarial education syllabus.
This book is available under a Creative Commons Attribution license (CC BY), which means that you are free to copy, distribute, and modify it, as long as you give appropriate credit to the original author.
If you enjoyed the book and would like to support the author, you can purchase a printed copy (hardcover or paperback) from official retailers.
Download and Read Links
Share this Book
[localhost]# find . -name "*Similar_Books*"
Deep Learning for Coders with Fastai and PyTorch
Deep learning is often viewed as the exclusive domain of math PhDs and big tech companies. But as this hands-on guide demonstrates, programmers comfortable with Python can achieve impressive results in deep learning with little math background, small amounts of data, and minimal code. How? With fastai, the first library to provide a consistent inte
Foundations of Machine Learning, 2nd Edition
A new edition of a graduate-level machine learning textbook that focuses on the analysis and theory of algorithms. This book is a general introduction to machine learning that can serve as a textbook for graduate students and a reference for researchers. It covers fundamental modern topics in machine learning while providing the theoretical basis a
Deep Learning with JavaScript
Deep learning has transformed the fields of computer vision, image processing, and natural language applications. Thanks to TensorFlow.js, now JavaScript developers can build deep learning apps without relying on Python or R. Deep Learning with JavaScript shows developers how they can bring DL technology to the web. Written by the main authors of t
Efficient Learning Machines
Machine learning techniques provide cost-effective alternatives to traditional methods for extracting underlying relationships between information and data and for predicting future events by processing existing information to train models. Efficient Learning Machines explores the major topics of machine learning, including knowledge discovery, cla
Machine Learning Yearning
AI is transforming numerous industries. Machine Learning Yearning, a free ebook from Andrew Ng, teaches you how to structure Machine Learning projects. This book is focused not on teaching you ML algorithms, but on how to make ML algorithms work. After reading Machine Learning Yearning, you will be able to: - Prioritize the most promising direction
Mobile Media Learning
This book is an inspirational message about what is possible and practical in the name of learning through mobile media. We present stories from a diverse set of educators, a microcosm of the landscape of mobile media learning. Each author has found a way to create something new and beautiful in their own world. And though their results are excepti