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Introduction to Probability Theory Lecture Note

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Name of Notes : – Introduction to Probability Theory Lecture Note

Introduction

Probability theory, a branch of mathematics concerned with the analysis of random phenomena. The outcome of a random event cannot be determined before it occurs, but it may be any one of several possible outcomes. The actual outcome is considered to be determined by chance.

The word probability has several meanings in ordinary conversation. Two of these are particularly important for the development and applications of the mathematical theory of probability. One is the interpretation of probabilities as relative frequencies, for which simple games involving coins, cards, dice, and roulette wheels provide examples. The distinctive feature of games of chance is that the outcome of a given trial cannot be predicted with certainty, although the collective results of a large number of trials display some regularity. For example, the statement that the probability of “heads” in tossing a coin equals one-half, according to the relative frequency interpretation, implies that in a large number of tosses the relative frequency with which “heads” actually occurs will be approximately one-half, although it contains no implication concerning the outcome of any given toss. There are many similar examples involving groups of people, molecules of a gas, genes, and so on. Actuarial statements about the life expectancy for persons of a certain age describe the collective experience of a large number of individuals but do not purport to say what will happen to any particular person. Similarly, predictions about the chance of a genetic disease occurring in a child of parents having a known genetic makeup are statements about relative frequencies of occurrence in a large number of cases but are not predictions about a given individual.

Modules / Lectures

• Introduction to Probability Theory
• Chapter 1 : Introduction
• Chapter 2 : Random Variables
• Chapter 3 : Conditional Probability and Independence
• Chapter 4 : Distributions
• Chapter 5 : Random Vectors , Joined Distributions
• Chapter 6 : Expectation and Conditional Expectation
• Chapter 7 : Characteristic Functions
• Chapter 8 : Limit Theorems