The Probability Theory Overview

Probability theory

Probably in all wakes of life, persistence leads to success. This can be true for several life situations. However, this probably does not fit in the world of gambling and games of chance. In gambling persistence probably has a very little role to play in determining whether or not the player would be successful at the game or not.


The probability theory is a branch of mathematics which analyzes random phenomena. The main objects in the probability theory are events, stochastic processes and random variables. Events are measured quantities which could be single occurrences or which might evolve in random fashion over time. An individual roll of a die or coin toss is considered to be a random event but if it is repeated several times the sequence of these random events will show a particular statistical pattern which can be studied and the future events can be predicted.

The probability theory is applied to several human activities which involve any type of quantitative analysis of large amount of data. The probability theory is also very widely applied to gambling and games of chance, especially to online roulette. This theory is also applied to description of several complex systems where there is only partial information available.

The probability theory and games of chance

The probability theory has its roots in the attempts which had been made in the 16th century by Gerolamo Cardano to study games of chance and in the 17th century by Blaise Pascal and Pierre de Fermat. In the year 1657 a book had been published by Christiaan Huygens on the subject. In most books where the probability theory is discussed in terms of gambling, the continuous probability distribution and the discrete probability distribution are treated separately. However, in more advanced books both of these can be treated together in any form. To study probability theory as it applies to gambling, it is necessary to first understand these two types of distributions.

Discrete probability distribution

The discrete probability distribution theory analyzes the events which occur in the countable sample spaces. The examples of these events could be the decks of cards and throwing dice. This type of distribution would study the number of cases which are favorable for the event or the total number of possible outcomes for an event. For example when a die is rolled, the probability is defined as 3/6 which is ½ since three out of those six faces have even numbers. Each face of the die has the same probability of appearing.

Continuous probability distribution

The continuous probability theory deals with the events which occur in continuous sample space. A distribution can be called continuous if it has a continuous cumulative distribution function. For example in discrete distribution, it is impossible to have an event with a zero probability like in the case of rolling of a die it is not possible to roll 3.5 on a die and the probability is zero. However, this is not the case in a continuous probability distribution.

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