# Theory of Probability and Statistics as Exemplified in Short Dictums

@inproceedings{Sheynin2009TheoryOP, title={Theory of Probability and Statistics as Exemplified in Short Dictums}, author={O. Sheynin}, year={2009} }

I am presenting a first-ever scientific collection of short sayings on probability and statistics expressed by most various men of science, many classics included, from antiquity to Kepler to our time. Quite understandably, the reader will find here no mathematical formulas and in some instances he will miss a worthy subject. Markov chains provide a good example: their inventor had not said anything about them suitable for my goal. Nevertheless, the scope of the collected dictums is amazingly… Expand

#### 2 Citations

Notes on stochastic processes Paul Keeler

- 2018

A stochastic process is a type of mathematical object studied in mathematics, particularly in probability theory, which can be used to represent some type of random evolution or change of a system.… Expand

Notes on stochastic processes

- 2016

A stochastic process is a type of mathematical object studied in mathematics, particularly in probability theory, which shows some type of a randomness. There are many types of stochastic processes… Expand

#### References

SHOWING 1-10 OF 112 REFERENCES

Kolmogorov's Contributions to the Foundations of Probability

- Mathematics, Computer Science
- Probl. Inf. Transm.
- 2003

The three stages of Kolmogorov's work on the foundations of probability are reviewed: his formulation of measure-theoretic probability, 1933; his frequentist theory of probability, 1963; and his algorithmic theory of randomness, 1965–1987. Expand

Bortkiewicz' Alleged Discovery: the Law of Small Numbers

- Mathematics
- 2008

Ladislaus von Bortkiewicz (1868-1931) published his law of small numbers in 1898. The name of that law was unfortunate; moreover, lacking any mathematical expression, it was only a principle. Many… Expand

Some Theory of Sampling

- Mathematics
- 1951

By W. Edwards Deming. A new book which presents the theoretical background plus the practical applications of modern statistical practice. Parts I, II, and III cover the problems which arise in… Expand

Notes on the method of least squares

- Mathematics
- 1933

In inferring the value of a physical quantity x from observations some risk must be accepted. It is therefore presumed that the investigator has made up his mind how much risk he will take, and… Expand

Statistically Speaking: A Dictionary of Quotations

- Computer Science
- 1996

The aim of this book is to clarify the role of statistics in the development of science and show how its use in decision-making has changed in the modern era. Expand

Probability and Finance: It's Only a Game!

- Economics
- 2001

Preface. Probability and Finance as a Game. PROBABILITY WITHOUT MEASURE. The Historical Context. The Bounded Strong Law of Large Numbers. Kolmogorov's Strong Law of Large Numbers. The Law of the… Expand

Marx, Kantorovich, and Novozhilov: Stoimost' versus Reality

- History
- 1961

Like other aspects of Soviet life, economics has been revivified by Stalin's death. The most visible part of its reawakening has been an extensive discussion of, and experimentation with,… Expand

Randomization and Social Affairs: The 1970 Draft Lottery

- Political Science, Medicine
- Science
- 1971

Since randomization does have a role in the everyday workings of society, it is important that the public be educated to accept the proper use of randomization, while rejecting attempts to use chance as a disguise for inequity, bias, and unlawful discrimination. Expand

Can an individual sequence of zeros and ones be random? Russian Math

- Mathematics
- 1990

CONTENTS Introduction Chapter I. The main notions and facts § 1.1. The notion of randomness depends on a given probability distribution § 1.2. Three faces of randomness: stochasticness, chaoticness,… Expand

An essay towards solving a problem in the doctrine of chances

- Sociology, Philosophy
- M.D. computing : computers in medical practice
- 1991

The probability of any event is the ratio between the value at which an expectation depending on the happening of the event ought to be computed, and the value of the thing expected upon it’s 2 happening. Expand