Discrete random variables

Lecture "Probability & statistics - Chapter 5: Discrete probability" has contents: Random variable, probability distribution, expected value, variance – standard deviation, bivariate probability, binomial distribution.

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Chapter 5 - Discrete random variables. After mastering the material in this chapter, you will be able to: Explain the difference between a discrete random variable and a continuous random variable, find a discrete probability distribution and compute its mean and standard deviation, use the binomial distribution to compute probabilities,...

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Lectures "Applied statistics for business - Chapter 5: Discrete probability distributions" provides students with the knowledge: Random variables, developing discrete probability distributions, expected value and variance, expected value and variance financial portfolios,... Invite you to refer to the disclosures.

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Let A be an n × n matrix, whose entries are independent copies of a centered random variable satisfying the subgaussian tail estimate. We prove that the operator norm of A−1 does not exceed Cn3/2 with probability close to 1. 1. Introduction Let A be an n × n matrix, whose entries are independent, identically distributed random variables. The spectral properties of such matrices, in particular invertibility, have been extensively studied (see, e.g. [M] and the survey [DS]).

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