William Feller

William "Vilim" Feller (July 7, 1906 – January 14, 1970), born Vilibald Srećko Feller, was a Croatian-American mathematician specializing in probability theory. Feller was one of the greatest probabilists of the twentieth century, who is remembered for his championing of probability theory as a branch of mathematical analysis in Sweden and the United States.

Quotes

An Introduction To Probability Theory And Its Applications (Third Edition)

• When this book was first conceived (more than 25 years ago) few mathematicians outside the Soviet Union recognized probability as a legitimate branch of mathematics.
  • Preface to the Third Edition, p. vii.

• The results concerning fluctuations in coin tossing show the widely held beliefs about the law of large numbers are fallacious. They were so amazing and so at variance with common intuition that even sophisticated colleagues doubted that coins actually misbehave as theory predicts.
  • Note on the Use of this Book, p. xi-xii.

• The bewildered novice in chess moves cautiously, recalling individual rules, whereas the experienced player absorbs a complicated situation at a glance and is unable to account rationally for his intuition. In like manner mathematical intuition grows with experience, and it is possible to develop a natural feeling for concepts such as four dimensional space.
  • Introduction, The Nature of Probability Theory, p. 2.

• The manner in which mathematical theories are applied does not depend on preconceived ideas; it is a purposeful technique depending on, and changing with, experience.
  • Introduction, The Nature of Probability Theory, p. 2 - 3.

• The philosophy of the foundations of probability must be divorced from mathematics and statistics, exactly as the discussion of our intuitive space concept is now divorced from geometry.
  • Introduction, The Nature of Probability Theory, p. 3.

• Historically, the original purpose of the theory of probability was to describe the exceedingly narrow domain of experience connected with games of chance, and the main effort was directed to the calculation of certain probabilities.
  • Introduction, The Nature of Probability Theory, p. 3.

• Only yesterday the practical things of today were decried as impractical, and the theories which will be practical tomorrow will always be branded as valueless games by the practical man of today.
  • Introduction, The Nature of Probability Theory, p. 6.

• When a coin is tossed, it does not necessarily fall heads or tails; it can roll away or stand on its edge.
  • Chapter I, The Sample Space, p. 7

It has been suggested that an army of monkeys might be trained to pound typewriters at random in the hope that ultimately great works of literature would be produced. Using a coin for the same purpose may save feeding and training expenses and free the monkeys for other monkey business.

• This means that if in a city seven accidents occur each week, then (assuming that all possible distributions are equally likely) practically all weeks will contain days with two or more accidents, and on the average only one week out of 165 will show a uniform distribution of one accident per day.
  • Chapter II, Elements Of Combinatorial Analysis, p. 32.

• Simple methods will soon lead us to results of far reaching theoretical and practical importance. We shall encounter theoretical conclusions which not only are unexpected but actually come as a shock to intuition and common sense. They will reveal that commonly accepted notions concerning chance fluctuations are without foundation and that the implications of the law of large numbers are widely misconstrued.
  • Chapter III, Fluctuations In Coin Tossing And Random Walks, p. 67.

• To every event defined for the original random walk there corresponds an event of equal probability in the dual random walk, and in this way almost every probability relation has its dual.
  • Chapter III, Fluctuations In Coin Tossing And Random Walks, p. 92.

• The notion of conditional probability is a basic tool of probability theory, and it is unfortunate that its great simplicity is somewhat obscured by a singularly clumsy terminology.
  • Chapter V, Conditional Probability, Stochastic Independence, p. 114.

• Infinite product spaces are the natural habitat of probability theory.
  • Chapter V, Conditional Probability, Stochastic Independence, p. 130

It must be understood that a fair game may be distinctly unfavorable to the player.

• The theory of independent experiments is the analytically simplest and most advanced part of probability theory.
  • Chapter V, Conditional Probability, Stochastic Independence, p. 132.

• It is a common fallacy to believe that the law of large numbers acts as a force endowed with memory seeking to return to the original state, and many wrong conclusions have been drawn from this assumption.
  • Chapter V, Conditional Probability, Stochastic Independence, p. 136.

• The man in the street, and also the philosopher K. Marbe, believe that after a run of seventeen heads tail becomes more probable. This argument has nothing to do with imperfections of physical coins; it endows nature with memory, or, in our terminology, it denies the stochastic independence of successive trials. Marbe's theory cannot be refuted by logic but is rejected because of empirical support.
  • Chapter VI, The Binomial And The Poisson Distributions, p. 147.

• Warning. It is usual to read into the law of large numbers things which it definitely does not imply. If Peter and Paul toss a perfect coin 10,000 times, it is customary to expect that Peter will be in the lead roughly half the time. This is not true. In a large number of different coin-tossing games it is reasonable to expect that any fixed moment heads will be in the lead in roughly half of all cases. But it is quiet likely that the player who ends at the winning side has been in the lead for practically the whole duration of the game. Thus, contrary to widespread belief, the time average for any individual game has nothing to do with the ensemble average at any given moment.
  • Chapter VI, The Binomial And The Poisson Distributions, p. 152-153.

• The painful experience of many gamblers has taught us the lesson that no system of betting is successful in improving the gambler's chances. If the theory of probability is true to life, this experience must correspond to a provable statement.
  • Chapter VIII, Unlimited Sequences Of Bernoulli Trials, p. 198.

• Note the situation is different when the player is permitted to vary his stakes. In this case there exist advantageous strategies, and the game depends on the strategy.
  • Chapter VIII, Unlimited Sequences Of Bernoulli Trials, p. 200

It is seen that continued shuffling may reasonably be expected to produce perfect "randomness" and to eliminate all traces of the original order. It should be noted, however, that the number of operations required for this purpose is extremely large.

• It has been suggested that an army of monkeys might be trained to pound typewriters at random in the hope that ultimately great works of literature would be produced. Using a coin for the same purpose may save feeding and training expenses and free the monkeys for other monkey business.
  • Chapter VIII, Unlimited Sequences Of Bernoulli Trials, p. 202.

• The classical theory of probability was devoted mainly to a study of the gamble's gain, which is again a random variable; in fact, every random variable can be interpreted as the gain of a real or imaginary gambler in a suitable game.
  • Chapter IX, Random Variables; Expectation, p. 212.

• Much harm was done by the misleading suggestive power of this name. It must be understood that a fair game may be distinctly unfavorable to the player.
  • Chapter X, Law Of large Numbers, p. 249.

• This faulty intuition as well as many modern applications of probability theory are under the strong influence of traditional misconceptions concerning the meaning of the law of large numbers and of a popular mystique concerning a so-called law of averages.
  • Chapter X, Law Of large Numbers, p. 250.

• It is not necessary to think of gambling places; the statistician who applies statistical tests is engaged in a dignified sort of gambling, and in his case the distribution of the random variables changes from occasion to occasion.
  • Chapter X, Law Of large Numbers, p. 253.

• The fact that the mean recurrence time is infinite implies that the chance fluctuations in an individual prolonged coin-tossing game are far removed from the familiar pattern governed by the normal distribution.
  • Chapter XIII, Recurrent Events. Renewal Theory. p. 314.

• Physical irreversibility manifests itself in the fact that, whenever the system is in a state far removed from equilibrium, it is much more likely to move toward equilibrium, than in the opposite direction.
  • Chapter XV, Markov Chains, p. 397.

• It is seen that continued shuffling may reasonably be expected to produce perfect "randomness" and to eliminate all traces of the original order. It should be noted, however, that the number of operations required for this purpose is extremely large.
  • Chapter XV, Markov Chains, p. 407.

• In stochastic processes the future is not uniquely determined, but we have at least probability relations enabling us to make predictions.
  • Chapter XV, Markov Chains, p. 420.

• It should be noted that this duration is considerably longer than we naively expect. If two players with 500 dollars each toss a coin until one is ruined, the average duration of the game is 250,000 trials. If a gambler has only one dollar and his adversary has 1000, the average duration is 1000 trials.
  • Chapter XIV, Random Walk And Ruin Problems, p. 349.

• " three repairman per twenty machines are much more economical than one repairman per six machines."
  • Chapter XVII, The Simplest Time Dependent Stochastic Processes, p. 466.

Quotes about Feller

• Anyone writing a probability text today owes a great debt to William Feller, who taught us all how to make probability come alive as a subject matter. If you find an example, an application, or an exercise that you really like, it probably had its origin in Feller's classic text, An Introduction to Probability Theory And (Its) Applications.
  • J. Laurie Snell, Introduction To Probability, p. viii.