How to Gamble If You Must: Inequalities for Stochastic Processes

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.

Lester E. Dubins (1920–2010) was Professor of Mathematics at the University of California, Berkeley, from 1962 to 2004. Leonard J. Savage (1917–1971) was a mathematician and statistician who taught at several universities, including Princeton, Yale, and Columbia. His other Dover book is The Foundation of Statistics.
William Sudderth is a Professor in the School of Statistics at the University of Minnesota. David Gilat is a Professor in the School of Mathematical Sciences at Tel Aviv University.