Posted by **exLib** at July 4, 2013

World Scientific Publishing | 2001 | ISBN: 9810248652 9789810248659 9789812799548 | 438 pages | PDF | 15 MB

This volume is a collection of articles on system and Bayesian reliability analysis. The book is dedicated to Emeritus Professor Richard E. Barlow, who is well known for his pioneering research on reliability theory and Bayesian reliability analysis.

Posted by **DZ123** at April 26, 2018

English | 2011 | ISBN: 3642183239 | PDF | pages: 405 | 3.5 mb

Posted by **Underaglassmoon** at April 26, 2018

Stanford Business | English | 2018 | ISBN-10: 0804793611 | 344 pages | PDF | 4.31 MB

by Ranga Ramanujam (Editor), Karlene H. Roberts (Editor)

Posted by **tarantoga** at Jan. 29, 2018

ISBN: 1118517075 | 2014 | EPUB | 256 pages | 39 MB

Posted by **arundhati** at Oct. 23, 2017

2014 | ISBN-10: 1447163079 | 300 pages | PDF | 3,8 MB

Posted by **interes** at Oct. 19, 2017

English | 2014 | ISBN: 1107007577 | 288 pages | PDF | 2 MB

Posted by **DZ123** at Aug. 30, 2015

English | 2008 | ISBN: 0387731717 | PDF | pages: 232 | 3,8 mb

Posted by **ChrisRedfield** at Oct. 20, 2014

Published: 2007-03-26 | ISBN: 0387707298, 1441943579, 0387707301 | PDF | 430 pages | 3 MB

Posted by **melia** at Jan. 31, 2013

English | 2007 | ISBN: 0387707298 | 430 pages | PDF | 3.5 MB

Posted by **ertugrul ergun** at June 4, 2007

Springer | ISBN / ASIN:0387707298 | 2007 | 430 pages | PDF | 3.3MB

This book presents applications of semi-Markov processes in finance, insurance and reliability, using real-life problems as examples. After a presentation of the main probabilistic tools necessary for understanding of the book, the authors show how to apply semi-Markov processes in finance, starting from the axiomatic definition and continuing eventually to the most advanced financial tools, particularly in insurance and in risk-and-ruin theories. Also considered are reliability problems that interact with credit risk theory in finance. The unique approach of this book is to solve finance and insurance problems with semi-Markov models in a complete way and furthermore present real-life applications of semi-Markov processes.