Mathematical Theory of Probability and Statistics

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We all know that some random events are more likely to occur than others, but how do you quantify such differences?

History of Probablility and Statistics

How do you work out the probability of, say, rolling a 6 on a die? Mathematicians avoid these tricky questions by defining the probability of an event mathematically without going into its deeper meaning. At the heart of this definition are three conditions, called the axioms of probability theory. As we have presented them here, these axioms are a simplified version of those laid down be the mathematician Andrey Kolmogorov in Up until then, the basic concepts of probability theory had been "considered to be quite peculiar" Kolmogorov's words so his aim was to put them in their "natural place, among the general notions of modern mathematics".

To this end Kolmogorov also gave a precise mathematical definition in terms of sets of what is meant by a "random event". We have left this bit out when stating the axioms above, but you can read about it in Kolmogorov's original text Foundations of the theory of probability.

1st Edition

With his axioms Kolmogorov put probability into the wider context of measure theory. When you are measuring something such as length, area or volume you are assigning a number to some sort of mathematical object a line segment, a 2D shape, or a 3D shape. In a similar way, probability is also a way of assigning a number to a mathematical object collections of events. Kolmogorov's formulation meant that the mathematical theory of measures could encompass the theory of probability as a special case.

If you are familiar with probability you might feel that two central ideas of the theory are missing from the above axioms. Free Shipping Free global shipping No minimum order. Preface Chapter I Fundamentals A. The Label Space Frequency. Chance Randomness The Collective B. Distribution Function Discrete Case. Measure-Theoretical Approach B.

Non-Countable Label Space. Riemann-Stieltjes Integral. Tchebycheff's Inequality Expectation Relative to a Distribution. Needle Problem B.

Probability Theory and Mathematical Statistics for Engineers

Khintchine's Theorem. Asymptotic Results for Infinite Products. Stirling's Formula. Probability Density. Central Limit Theorem. Probability of the Sum of Rare Events.

Maths in a minute: The axioms of probability theory |

Bayes' Method A. Irrelevance of p0 x for large n C. Composite Hypothesis.

Probability: What is Probability & Definition of Chance, Trial, Random Experiment, Event -STD IX: 01

Correlation A. Continuity, Differentiability Higher Derivatives. Taylor's Theorem B.

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