Stochastic Control in Insurance (Probability and its Applications) 在線電子書 pdf 下載 txt下載 epub 下載 mobi 下載 2024

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Hanspeter Schmidli 作者

Springer

譯者

2007-12-21 出版日期

254 頁數

USD 79.95 價格

Paperback

Probability and its Applications- A Series of the Applied Probability Trust 叢書系列

9781848000025 圖書編碼

Stochastic Control in Insurance (Probability and its Applications) 在線電子書圖書標籤: 隨機控製金融數學精算係列數學

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發表於2024-07-06

Stochastic Control in Insurance (Probability and its Applications) 在線電子書 epub 下載 mobi 下載 pdf 下載 txt 下載 2024

Stochastic Control in Insurance (Probability and its Applications) 在線電子書 epub 下載 pdf 下載 mobi 下載 txt 下載 2024

Stochastic Control in Insurance (Probability and its Applications) 在線電子書 pdf 下載 txt下載 epub 下載 mobi 下載 2024

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Stochastic Control in Insurance (Probability and its Applications) 在線電子書 pdf 下載 txt下載 epub 下載 mobi 在線電子書下載

Stochastic Control in Insurance (Probability and its Applications) 在線電子書圖書描述

Stochastic control is one of the methods being used to find optimal decision-making strategies in fields such as operations research and mathematical finance. In recent years, stochastic control techniques have been applied to non-life insurance problems, and in life insurance the theory has been further developed. This book provides a systematic treatment of optimal control methods applied to problems from insurance and investment, complete with detailed proofs. The theory is discussed and illustrated by way of examples, using concrete simple optimisation problems that occur in the actuarial sciences. The problems come from non-life insurance as well as life and pension insurance and also cover the famous Merton problem from mathematical finance. Wherever possible, the proofs are probabilistic but in some cases well-established analytical methods are used. The book is directed towards graduate students and researchers in actuarial science and mathematical finance who want to learn stochastic control within an insurance setting, but it will also appeal to applied probabilists interested in the insurance applications and to practitioners who want to learn more about how the method works. Readers should be familiar with basic probability theory and have a working knowledge of Brownian motion, Markov processes, martingales and stochastic calculus. Some knowledge of measure theory will also be useful for following the proofs.

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