TAILIEUCHUNG - Independent And Stationary Sequences Of Random Variables

This chapter is of an introductory nature, its purpose being to indicate some concepts and results from the theory of probability which are used in later chapters . Most of these are contained in Chapters 1-9 of Gnedenko [47], and will therefore be cited without proof. The first section is somewhat isolated, and contains a series of results from the foundations of the theory of probability. A detailed account may be found in [76], or in Chapter I of [31] . Some of these will not be needed in the first part of the book, in which attention is confined to independent random variables | Chapter 1 PROBABILITY DISTRIBUTIONS ON THE REAL LINE INFINITELY DIVISIBLE LAWS This chapter is of an introductory nature its purpose being to indicate some concepts and results from the theory of probability which are used in later chapters. Most of these are contained in Chapters 1-9 of Gnedenko 47 and will therefore be cited without proof. The first section is somewhat isolated and contains a series of results from the foundations of the theory of probability. A detailed account may be found in 76 or in Chapter I of 31 . Some of these will not be needed in the first part of the book in which attention is confined to independent random variables. 1. Probability spaces conditional probabilities and expectations A probability space is a triple Í2 5 p where Í2 is a set of elements ứ 5 a ơ-algebra of subsets of Í2 called events and p a measure on 5 with p Q 1. For E e p is called the probability of the event E. A random variable X is a real-valued measurable function on Í2 5 and the measure F defined on the Borel sets of the real line R by F A p x e A is called the distribution of X. Several random variables xlf x2 . Xn may be combined in a random vector X Xỵ x2 . Xn and the measure F A P Xe A defined on the Borel sets of R is the distribution of X or the joint distribution of the variables xt x2 x . More generally if T is any set of real numbers a family of random variables X i teT defined on Í2 5 p is called a random process. Conditions for the existence of random processes with prescribed joint distributions are given by Kolmogorov s theorem 76 . A probability space is a special case of a measurable space and it is there 18 PROBABILITY DISTRIBUTIONS ON THE REAL LINE Chap. 1 fore possible to construct in it a Lebesgue integral as for example in 105 . If the function X is integrable with respect to p that is if í ỊX co I p dco 00 J ÍÌ then the integral f X co P dco XdP J Q Ji is called the expectation of X and is denoted by the symbol E X . If X is a random vector .

• Nông nghiệp
• Random Variables
• theory of probability
• Probability spaces
• distribution functions
• Convergence of distributions
• Continuity
• Probability random variables and stochastic processes
• Probability random variables
• Stochastic processes
• Probability variables
• Lecture Probability Theory
• Probability Theory
• Lý thuyết xác suất
• Distribution Function
• Continuous type random variables
• Discrete type random variables
• Two Random Variables
• Joint probability density function
• Multiple Random Variables
• Hai biến ngẫu nhiên
• Complete convergence
• M pairwise negatively dependent random variables
• Pairwise independent
• Identically distributed random variables
• Negative association
• Pairwise negative dependence
• A first course in probability
• Combinatorial analysis
• Axioms of probability
• Conditional probability
• Continuous random variables
• Jointly distributed random variables
• Two Functions of Two Random Variables
• Moments equalities
• Integer valued
• Equalities generalize
• Cumulative distribution function
• Nonnegative random variable
• One Function of Two Random Variables
• Poisson random
• distributions functions
• 2 dimensions 3
• Inequalities between
• higher order
• Mean
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• special cases
• Applied statistics and probability
• Probability distributions
• Discrete random variables
• Joint probability distributions
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