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Inequalities for Stochastic Processes: How to Gamble If You Must

door Lester E. Dubins, Leonard J. Savage (Auteur)

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This classic of advanced statistics is geared toward graduate-level readers and uses the concepts of gambling to develop important ideas in probability theory. The authors have distilled the essence of many years' research into a dozen concise chapters. ""Strongly recommended"" by the Journal of the American Statistical Association upon its initial publication, this revised and updated edition features contributions from two well-known statisticians that include a new Preface, updated references, and findings from recent research. Following an introductory chapter, the book formulates the gambler's problem and discusses gambling strategies. Succeeding chapters explore the properties associated with casinos and certain measures of subfairness. Concluding chapters relate the scope of the gambler's problems to more general mathematical ideas, including dynamic programming, Bayesian statistics, and stochastic processes.… (meer)
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Professor Graciela Chichilnisky has chosen to discuss Inequalities for Stochastic Processes: How to Gamble if You Must by Leonard Savage and Lester Dubins on FiveBooks as one of the top five on her subject - Risk Management, saying that:



“…Yes, it doesn’t fit the sequence like my last books do but it is very interesting. Perhaps it is not as well known as it should be. It does something really simple and yet incredibly complicated. The book develops Savage’s idea in the context of gambling and says suppose that you need a certain amount of money by a certain date and you go to a casino to gamble. How should you play?



Imagine yourself at a casino with $1,000. For some reason you desperately need $10,000 by morning; anything less is worth nothing for your purpose. What ought you to do? How should you play? The only thing possible is to gamble away your last cent if need be in an attempt to reach the target sum of $10,000. The question is how to play, not whether.



The answer provided in this book may be surprising to many. Under the circumstances, an optimal strategy for you is to play boldly: that is, always to stake on each bet all the money in your possession or just enough to arrive immediately at the target sum in case you win the bet. It is ill advised, at a roulette table, to bet on more than one number at the same time.



What he shows is the strategy you should follow to get as safely as possible to the level of money you need. It is a matter of survival, strategies for survival.



Lester Dubins was a professor at UC Berkley when I was a student and did my PhD in mathematics. And he is now not well which is very sad. I tried to go and talk to him about my latest work but because he is ill it is very difficult. It is a great shame because I know he would have loved to talk through all my latest theories. …”



The full interview is available here: http://fivebooks.com/interviews/graciela-chichilnisky ( )
  FiveBooks | Apr 9, 2010 |
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» Andere auteurs toevoegen

AuteursnaamRolType auteurWerk?Status
Dubins, Lester E.Auteurprimaire auteuralle editiesbevestigd
Savage, Leonard J.Auteurprimaire auteuralle editiesbevestigd

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This classic of advanced statistics is geared toward graduate-level readers and uses the concepts of gambling to develop important ideas in probability theory. The authors have distilled the essence of many years' research into a dozen concise chapters. ""Strongly recommended"" by the Journal of the American Statistical Association upon its initial publication, this revised and updated edition features contributions from two well-known statisticians that include a new Preface, updated references, and findings from recent research. Following an introductory chapter, the book formulates the gambler's problem and discusses gambling strategies. Succeeding chapters explore the properties associated with casinos and certain measures of subfairness. Concluding chapters relate the scope of the gambler's problems to more general mathematical ideas, including dynamic programming, Bayesian statistics, and stochastic processes.

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