Mortality Table Construction PDF Download

Author: Robert W. Batten
Publisher: Prentice Hall
ISBN:
Category : Social Science
Languages : en
Pages : 264

View

Book Description

Author: Ray D. Murphy
Publisher:
ISBN:
Category : Life insurance
Languages : en
Pages : 88

View

Book Description

Author: National Academies of Sciences Engineering and Medicine
Publisher:
ISBN: 9780309684736
Category :
Languages : en
Pages :

View

Book Description

Author: Jeffrey K. Dellinger
Publisher: John Wiley & Sons
ISBN: 047177376X
Category : Business & Economics
Languages : en
Pages : 802

View

Book Description
In-depth coverage of variable income annuities With trillions of dollars in retirement savings assets, the tens of millions of Americans on the precipice of retirement need to convert these savings into retirement income. The fact that variable income annuities (VIAs) generate maximum lifetime income with zero probability of outliving it has spurred the need for more information about VIAs. The Handbook of Variable Income Annuities is by far the most comprehensive source of information on this topic. This book thoroughly describes the most important principles of optimal asset liquidation and demystifies VIA mechanics, so readers can gain a high comfort level with this important financial instrument. Interestingly and clearly, The Handbook of Variable Income Annuities explains the mathematical pricing of variable income annuities, expected rates of return, taxation, product distribution, legal aspects, and much more. Jeffrey K. Dellinger (Fort Wayne, IN), a Fellow of the Society of Actuaries and a member of the American Academy of Actuaries, has over 25 years experience in the financial services sector. He advises institutions on retirement income optimization, products, and markets.

Author: Andrew Leung
Publisher: Academic Press
ISBN: 0323901735
Category : Business & Economics
Languages : en
Pages : 266

View

Book Description
Actuarial Principles: Lifetables and Mortality Models explores the core of actuarial science: the study of mortality and other risks and applications. Including the CT4 and CT5 UK courses, but applicable to a global audience, this work lightly covers the mathematical and theoretical background of the subject to focus on real life practice. It offers a brief history of the field, why actuarial notation has become universal, and how theory can be applied to many situations. Uniquely covering both life contingency risks and survival models, the text provides numerous exercises (and their solutions), along with complete self-contained real-world assignments. - Provides detailed coverage of life contingency risks and survival models - Presents self-contained chapters with coverage of key topics from both practitioner and theoretical viewpoints - Includes numerous real world exercises that are accompanied by enlightening solutions - Covers useful background information on how and why the subject has evolved and developed

Author: Chin Long Chiang
Publisher:
ISBN:
Category : Medical
Languages : en
Pages : 344

View

Book Description
The statistical theory of the ordinary life table is presented in this book, with its construction explained. Topics cover measures of mortality and adjustment of rates, and other areas include survival and stages of disease, reproduction, married life, antenatal life table and ecological studies.

Author: Thomas Nall Eden Greville
Publisher:
ISBN:
Category : Life insurance
Languages : en
Pages : 168

View

Book Description

Author:
Publisher:
ISBN:
Category : Administrative law
Languages : en
Pages : 1364

View

Book Description
Special edition of the Federal Register, containing a codification of documents of general applicability and future effect ... with ancillaries.

Author: United States. Bureau of the Census
Publisher:
ISBN:
Category :
Languages : en
Pages : 510

View

Book Description

Author: Giancarlo Zaccone
Publisher: Packt Publishing Ltd
ISBN: 1786460122
Category : Computers
Languages : en
Pages : 316

View

Book Description
Delve into neural networks, implement deep learning algorithms, and explore layers of data abstraction with the help of this comprehensive TensorFlow guide About This Book Learn how to implement advanced techniques in deep learning with Google's brainchild, TensorFlow Explore deep neural networks and layers of data abstraction with the help of this comprehensive guide Real-world contextualization through some deep learning problems concerning research and application Who This Book Is For The book is intended for a general audience of people interested in machine learning and machine intelligence. A rudimentary level of programming in one language is assumed, as is a basic familiarity with computer science techniques and technologies, including a basic awareness of computer hardware and algorithms. Some competence in mathematics is needed to the level of elementary linear algebra and calculus. What You Will Learn Learn about machine learning landscapes along with the historical development and progress of deep learning Learn about deep machine intelligence and GPU computing with the latest TensorFlow 1.x Access public datasets and utilize them using TensorFlow to load, process, and transform data Use TensorFlow on real-world datasets, including images, text, and more Learn how to evaluate the performance of your deep learning models Using deep learning for scalable object detection and mobile computing Train machines quickly to learn from data by exploring reinforcement learning techniques Explore active areas of deep learning research and applications In Detail Deep learning is the step that comes after machine learning, and has more advanced implementations. Machine learning is not just for academics anymore, but is becoming a mainstream practice through wide adoption, and deep learning has taken the front seat. As a data scientist, if you want to explore data abstraction layers, this book will be your guide. This book shows how this can be exploited in the real world with complex raw data using TensorFlow 1.x. Throughout the book, you'll learn how to implement deep learning algorithms for machine learning systems and integrate them into your product offerings, including search, image recognition, and language processing. Additionally, you'll learn how to analyze and improve the performance of deep learning models. This can be done by comparing algorithms against benchmarks, along with machine intelligence, to learn from the information and determine ideal behaviors within a specific context. After finishing the book, you will be familiar with machine learning techniques, in particular the use of TensorFlow for deep learning, and will be ready to apply your knowledge to research or commercial projects. Style and approach This step-by-step guide will explore common, and not so common, deep neural networks and show how these can be exploited in the real world with complex raw data. With the help of practical examples, you will learn how to implement different types of neural nets to build smart applications related to text, speech, and image data processing.