Mathematical And Statistical Methods For Actuarial Sciences Volume I
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Mathematical And Statistical Methods For Actuarial Sciences Volume I
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Author : Malcolm McNab
language : en
Publisher:
Release Date : 2025-08-25
Mathematical And Statistical Methods For Actuarial Sciences Volume I written by Malcolm McNab and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-08-25 with Business & Economics categories.
Mathematical and statistical methods are essential in actuarial sciences for assessing risk and uncertainty in insurance, pensions and finance. Key mathematical tools include calculus, probability and financial mathematics. Statistical methods involve analysing data, modelling and using regression techniques to predict future events and trends. These methods help in pricing insurance policies, setting aside reserves for future claims and managing financial risks. By combining these tools, professionals in actuarial sciences can make informed decisions, ensuring financial stability and protecting companies from potential losses. Overall, these methods form the foundation of actuarial work, enabling the prediction and management of future financial risks effectively. This book is a compilation of chapters that discuss the most vital concepts in the field of mathematics and statistics. The topics covered in this extensive book deal with the core subjects of actuarial sciences. This book, with its detailed analyses and data, will prove immensely beneficial to professionals and students involved in this area at various levels.
Mathematical And Statistical Methods For Actuarial Sciences And Finance
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Author : Marco Corazza
language : en
Publisher: Springer Nature
Release Date : 2024-08-01
Mathematical And Statistical Methods For Actuarial Sciences And Finance written by Marco Corazza and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-08-01 with Mathematics categories.
The book presents a collection of peer-reviewed short papers selected from those presented at the International Conference Mathematical and Statistical Methods for Actuarial Sciences and Finance – MAF2024. The year 2024 marks the twentieth anniversary of the first edition of this conference. The idea behind this book is that collaboration and cross-pollination between mathematicians and statisticians working in actuarial sciences and finance could enhance research in these fields. The effectiveness of this concept has been demonstrated by widespread participation across all editions, held at various locations including the University of Salerno, Italy (2004, 2006, 2010, 2014, and 2022); Ca' Foscari University of Venice, Italy (2008, 2012, and 2020); University Paris-Dauphine in Paris, France (2016); University Carlos III of Madrid, Madrid (2018); and University of Le Havre Normandie, Le Havre, France (2024). This effectiveness is also evident in the attention consistently shown by both the scientific community and professionals toward the volumes of peer-reviewed papers accompanying all past editions of MAF. The book is a valuable resource for academics, researchers, Ph.D. students, and professionals. Furthermore, it is also of interest to other readers with a quantitative background.
Mathematical And Statistical Methods For Actuarial Sciences And Finance
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Author : Cira Perna
language : en
Publisher: Springer Science & Business Media
Release Date : 2012-03-08
Mathematical And Statistical Methods For Actuarial Sciences And Finance written by Cira Perna and has been published by Springer Science & Business Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2012-03-08 with Mathematics categories.
The book develops the capabilities arising from the cooperation between mathematicians and statisticians working in insurance and finance fields. It gathers some of the papers presented at the conference MAF2010, held in Ravello (Amalfi coast), and successively, after a reviewing process, worked out to this aim.
Mathematical And Statistical Methods For Actuarial Sciences Volume Ii
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Author : Malcolm McNab
language : en
Publisher:
Release Date : 2025-08-25
Mathematical And Statistical Methods For Actuarial Sciences Volume Ii written by Malcolm McNab and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-08-25 with Business & Economics categories.
Mathematical and statistical methods are essential in actuarial sciences for assessing risk and uncertainty in insurance, pensions and finance. Key mathematical tools include calculus, probability and financial mathematics. Statistical methods involve analysing data, modelling and using regression techniques to predict future events and trends. These methods help in pricing insurance policies, setting aside reserves for future claims and managing financial risks. By combining these tools, professionals in actuarial sciences can make informed decisions, ensuring financial stability and protecting companies from potential losses. Overall, these methods form the foundation of actuarial work, enabling the prediction and management of future financial risks effectively. This book is a compilation of chapters that discuss the most vital concepts in the field of mathematics and statistics. The topics covered in this extensive book deal with the core subjects of actuarial sciences. This book, with its detailed analyses and data, will prove immensely beneficial to professionals and students involved in this area at various levels.
Mathematical And Statistical Methods For Actuarial Sciences And Finance
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Author : Marco Corazza
language : en
Publisher: Springer Nature
Release Date : 2022-04-11
Mathematical And Statistical Methods For Actuarial Sciences And Finance written by Marco Corazza and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022-04-11 with Mathematics categories.
The cooperation and contamination among mathematicians, statisticians and econometricians working in actuarial sciences and finance are improving the research on these topics and producing numerous meaningful scientific results. This volume presents new ideas in the form of four- to six-page papers presented at the International Conference MAF2022 – Mathematical and Statistical Methods for Actuarial Sciences and Finance. Due to the COVID-19 pandemic, the conference, to which this book is related, was organized in a hybrid form by the Department of Economics and Statistics of the University of Salerno, with the partnership of the Department of Economics of Cà Foscari University of Venice, and was held from 20 to 22 April 2022 in Salerno (Italy) MAF2022 is the tenth edition of an international biennial series of scientific meetings, started in 2004 on the initiative of the Department of Economics and Statistics of the University of Salerno. It has established itself internationally with gradual and continuous growth and scientific enrichment. The effectiveness of this idea has been proven by the wide participation in all the editions, which have been held in Salerno (2004, 2006, 2010, 2014, 2022), Venice (2008, 2012 and 2020 online), Paris (2016) and Madrid (2018). This book covers a wide variety of subjects: artificial intelligence and machine learning in finance and insurance, behavioural finance, credit risk methods and models, dynamic optimization in finance, financial data analytics, forecasting dynamics of actuarial and financial phenomena, foreign exchange markets, insurance models, interest rate models, longevity risk, models and methods for financial time series analysis, multivariate techniques for financial markets analysis, pension systems, portfolio selection and management, real-world finance, risk analysis and management, trading systems, and others. This volume is a valuable resource for academics, PhD students, practitioners, professionals and researchers. Moreover, it is also of interest to other readers with quantitative background knowledge.
The History Of Actuarial Science Vol I
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Author : Steven Haberman
language : en
Publisher: Taylor & Francis
Release Date : 2024-10-28
The History Of Actuarial Science Vol I written by Steven Haberman and has been published by Taylor & Francis this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-10-28 with Business & Economics categories.
A book which covers the key period in the history of actuarial science from the mid-17th century to the early 19th century. There are reprints of the most important treatises, pamphlets, tables and writings which trace the development of the actuarial industry.
Mathematical And Statistical Methods For Actuarial Sciences And Finance
DOWNLOAD
Author : Marco Corazza
language : en
Publisher:
Release Date : 2024
Mathematical And Statistical Methods For Actuarial Sciences And Finance written by Marco Corazza and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024 with Actuarial science categories.
The book presents a collection of peer-reviewed short papers selected from those presented at the International Conference Mathematical and Statistical Methods for Actuarial Sciences and Finance - MAF2024. The year 2024 marks the twentieth anniversary of the first edition of this conference. The idea behind this book is that collaboration and cross-pollination between mathematicians and statisticians working in actuarial sciences and finance could enhance research in these fields. The effectiveness of this concept has been demonstrated by widespread participation across all editions, held at various locations including the University of Salerno, Italy (2004, 2006, 2010, 2014, and 2022); Ca' Foscari University of Venice, Italy (2008, 2012, and 2020); University Paris-Dauphine in Paris, France (2016); University Carlos III of Madrid, Madrid (2018); and University of Le Havre Normandie, Le Havre, France (2024). This effectiveness is also evident in the attention consistently shown by both the scientific community and professionals toward the volumes of peer-reviewed papers accompanying all past editions of MAF. The book is a valuable resource for academics, researchers, Ph.D. students, and professionals. Furthermore, it is also of interest to other readers with a quantitative background.
Matrix Algebra Useful For Statistics
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Author : Shayle R. Searle
language : en
Publisher: John Wiley & Sons
Release Date : 2006-03-20
Matrix Algebra Useful For Statistics written by Shayle R. Searle and has been published by John Wiley & Sons this book supported file pdf, txt, epub, kindle and other format this book has been release on 2006-03-20 with Mathematics categories.
WILEY-INTERSCIENCE PAPERBACK SERIES The Wiley-Interscience Paperback Series consists of selected booksthat have been made more accessible to consumers in an effort toincrease global appeal and general circulation. With these newunabridged softcover volumes, Wiley hopes to extend the lives ofthese works by making them available to future generations ofstatisticians, mathematicians, and scientists. "This book is intended to teach useful matrix algebra to 'students,teachers, consultants, researchers, and practitioners' in'statistics and other quantitative methods'.The author concentrateson practical matters, and writes in a friendly and informal style .. . this is a useful and enjoyable book to have at hand." -Biometrics This book is an easy-to-understand guide to matrix algebra and itsuses in statistical analysis. The material is presented in anexplanatory style rather than the formal theorem-proof format. Thisself-contained text includes numerous applied illustrations,numerical examples, and exercises.
Approximate Dynamic Programming
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Author : Warren B. Powell
language : en
Publisher: John Wiley & Sons
Release Date : 2011-10-26
Approximate Dynamic Programming written by Warren B. Powell and has been published by John Wiley & Sons this book supported file pdf, txt, epub, kindle and other format this book has been release on 2011-10-26 with Mathematics categories.
Praise for the First Edition "Finally, a book devoted to dynamic programming and written using the language of operations research (OR)! This beautiful book fills a gap in the libraries of OR specialists and practitioners." —Computing Reviews This new edition showcases a focus on modeling and computation for complex classes of approximate dynamic programming problems Understanding approximate dynamic programming (ADP) is vital in order to develop practical and high-quality solutions to complex industrial problems, particularly when those problems involve making decisions in the presence of uncertainty. Approximate Dynamic Programming, Second Edition uniquely integrates four distinct disciplines—Markov decision processes, mathematical programming, simulation, and statistics—to demonstrate how to successfully approach, model, and solve a wide range of real-life problems using ADP. The book continues to bridge the gap between computer science, simulation, and operations research and now adopts the notation and vocabulary of reinforcement learning as well as stochastic search and simulation optimization. The author outlines the essential algorithms that serve as a starting point in the design of practical solutions for real problems. The three curses of dimensionality that impact complex problems are introduced and detailed coverage of implementation challenges is provided. The Second Edition also features: A new chapter describing four fundamental classes of policies for working with diverse stochastic optimization problems: myopic policies, look-ahead policies, policy function approximations, and policies based on value function approximations A new chapter on policy search that brings together stochastic search and simulation optimization concepts and introduces a new class of optimal learning strategies Updated coverage of the exploration exploitation problem in ADP, now including a recently developed method for doing active learning in the presence of a physical state, using the concept of the knowledge gradient A new sequence of chapters describing statistical methods for approximating value functions, estimating the value of a fixed policy, and value function approximation while searching for optimal policies The presented coverage of ADP emphasizes models and algorithms, focusing on related applications and computation while also discussing the theoretical side of the topic that explores proofs of convergence and rate of convergence. A related website features an ongoing discussion of the evolving fields of approximation dynamic programming and reinforcement learning, along with additional readings, software, and datasets. Requiring only a basic understanding of statistics and probability, Approximate Dynamic Programming, Second Edition is an excellent book for industrial engineering and operations research courses at the upper-undergraduate and graduate levels. It also serves as a valuable reference for researchers and professionals who utilize dynamic programming, stochastic programming, and control theory to solve problems in their everyday work.
Optimal Learning
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Author : Warren B. Powell
language : en
Publisher: John Wiley & Sons
Release Date : 2013-07-09
Optimal Learning written by Warren B. Powell and has been published by John Wiley & Sons this book supported file pdf, txt, epub, kindle and other format this book has been release on 2013-07-09 with Mathematics categories.
Learn the science of collecting information to make effective decisions Everyday decisions are made without the benefit of accurate information. Optimal Learning develops the needed principles for gathering information to make decisions, especially when collecting information is time-consuming and expensive. Designed for readers with an elementary background in probability and statistics, the book presents effective and practical policies illustrated in a wide range of applications, from energy, homeland security, and transportation to engineering, health, and business. This book covers the fundamental dimensions of a learning problem and presents a simple method for testing and comparing policies for learning. Special attention is given to the knowledge gradient policy and its use with a wide range of belief models, including lookup table and parametric and for online and offline problems. Three sections develop ideas with increasing levels of sophistication: Fundamentals explores fundamental topics, including adaptive learning, ranking and selection, the knowledge gradient, and bandit problems Extensions and Applications features coverage of linear belief models, subset selection models, scalar function optimization, optimal bidding, and stopping problems Advanced Topics explores complex methods including simulation optimization, active learning in mathematical programming, and optimal continuous measurements Each chapter identifies a specific learning problem, presents the related, practical algorithms for implementation, and concludes with numerous exercises. A related website features additional applications and downloadable software, including MATLAB and the Optimal Learning Calculator, a spreadsheet-based package that provides an introduction to learning and a variety of policies for learning.