This compact volume equips the reader with all the facts and principles essential to a fundamental understanding of the theory of probability. It is an introduction, no more: throughout the book the authors discuss the theory of probability for situations having only a finite number of possibilities, and the mathematics employed is held to the elementary level. But within its purposely restricted range it is extremely thorough, well organized, and absolutely authoritative. It is the only English translation of the latest revised Russian edition; and it is the only current translation on the market that has been checked and approved by Gnedenko himself.
After explaining in simple terms the meaning of the concept of probability and the means by which an event is declared to be in practice, impossible, the authors take up the processes involved in the calculation of probabilities. They survey the rules for addition and multiplication of probabilities, the concept of conditional probability, the formula for total probability, Bayes's formula, Bernoulli's scheme and theorem, the concepts of random variables, insufficiency of the mean value for the characterization of a random variable, methods of measuring the variance of a random variable, theorems on the standard deviation, the Chebyshev inequality, normal laws of distribution, distribution curves, properties of normal distribution curves, and related topics.
The book is unique in that, while there are several high school and college textbooks available on this subject, there is no other popular treatment for the layman that contains quite the same material presented with the same degree of clarity and authenticity. Anyone who desires a fundamental grasp of this increasingly important subject cannot do better than to start with this book. New preface for Dover edition by B. V. Gnedenko.
PART I. PROBABILITIES CHAPTER I. THE PROBABILITY OF AN EVENT 1. The concept of probability 2. Impossible and certain events 3. Problem CHAPTER 2. RULE FOR THE ADDITION OF PROBABILITIES 4. Derivation of the rule for the addition of probabilities 5. Complete system of events 6. Examples CHAPTER 3. CONDITIONAL PROBABILITIES AND THE MULTIPLICATION RULE 7. The concept of conditional probability 8. Derivation of the rule for the multiplication of probabilities 9. Independent events CHAPTER 4. CONSEQUENCES OF THE ADDITION AND MULTIPLICATION RULES 10. Derivation of certain inequalities 11. Formula for total probability 12. Bayes's formula CHAPTER 5. BERNOULLI'S SCHEME 13. Examples 14. The Bernoulli formulas 15. The most probable number of occurrences of an event CHAPTER 6 BERNOULLI'S THEOREM 16. Content of Bernoulli's theorem 17. Proof of Bernoulli's theorem PART II. RANDOM VARIABLES CHAPTER 7. RANDOM VARIABLES AND DISTRIBUTION LAWS 18. The concept of random variable 19. The concept of law of distribution CHAPTER 8. MEAN VALUES 20. Determination of the mean value of a random variable CHAPTER 9. MEAN VALUE OF A SUM AND OF A PRODUCT 21. Theorem on the mean value of a sum 22. Theorem on the mean value of a product CHAPTER 10. DISPERSION AND MEAN MEAN DEVIATIONS 23. Insufficiency of the mean value for the characterization of a random variable 24. Various methods of measuring the dispersion of a random variable 25. Theorems on the standard deviation CHAPTER 11. LAW OF LARGE NUMBERS 26. Chebyshev's inequality 27. Law of large numbers 28. Proof of the law of large numbers CHAPTER 12. NORMAL LAWS 29. Formulation of the problem 30. Concept of a distribution curve 31. Properties of normal distribution curves 32. Solution of problems CONCLUSION APPENDIX. Table of values of the function F (a) BIBLIOGRAPHY INDEX
