Mathematical Theory Of Probability And Statistics

Mathematical Theory of Probability and Statistics

Fundamentals; General label space; Basic properties of distributions; Examples of combined operations; Summation of chance variables characteristic function; Asymptotic distribution of the sum of chance variables; probability inference: Bayes' method; More on distributions; Analysis of statistical data; Problem of inference; Multivariate statistics: correlation; Introduction to the theory of statistical functions.

Mathematical Theory of Probability and Statistics

Mathematical Theory of Probability and Statistics focuses on the contributions and influence of Richard von Mises on the processes, methodologies, and approaches involved in the mathematical theory of probability and statistics. The publication first elaborates on fundamentals, general label space, and basic properties of distributions. Discussions focus on Gaussian distribution, Poisson distribution, mean value variance and other moments, non-countable label space, basic assumptions, operations, and distribution function. The text then ponders on examples of combined operations and summation of chance variables characteristic function. The book takes a look at the asymptotic distribution of the sum of chance variables and probability inference. Topics include inference from a finite number of observations, law of large numbers, asymptotic distributions, limit distribution of the sum of independent discrete random variables, probability of the sum of rare events, and probability density. The text also focuses on the introduction to the theory of statistical functions and multivariate statistics. The publication is a dependable source of information for researchers interested in the mathematical theory of probability and statistics

Mathematical Theory of Probability and Statistics