# 'Random variable' presentation slideshows

## Chapter 3, Section 9 Discrete Random Variables

Chapter 3, Section 9 Discrete Random Variables. Moment-Generating Functions.  John J Currano, 12/15/2008. æ. ö. k. (  c ) k  j E [ Y j ]. E [ ( Y – c ) k ]. k. =. å. ç. ÷. j. è. ø. =. j. 0. E [ ( Y – E ( Y ) ) k ] = E [ ( Y –  ) k ]. m. =. k.

By paul
(506 views)

## Section

Section Duration Data Introduction Sometimes we have data on length of time of a particular event or ‘spells’ Time until death Time on unemployment Time to complete a PhD

By paul
(316 views)

## RANDOM VARIABLES, EXPECTATIONS, VARIANCES ETC.

RANDOM VARIABLES, EXPECTATIONS, VARIANCES ETC. Variable. Recall: Variable: A characteristic of population or sample that is of interest for us. Random variable: A function defined on the sample space S that associates a real number with each outcome in S. DISCRETE RANDOM VARIABLES.

(369 views)

## M ARIO F . T RIOLA

S TATISTICS. E LEMENTARY. Chapter 4 Probability Distributions. M ARIO F . T RIOLA. E IGHTH. E DITION. Chapter 4 Probability Distributions. 4-1 Overview 4-2 Random Variables 4-3 Binomial Probability Distributions

By paul
(203 views)

## Stats for Engineers: Lecture 4

Stats for Engineers: Lecture 4. Summary from last time. Standard deviation – measure spread of distribution. Variance = (standard deviation) 2. Discrete Random Variables. Binomial distribution – number of successes from independent Bernoulli (YES/NO) trials.

By JasminFlorian
(433 views)

## Random Processes Introduction

Random Processes Introduction. Professor Ke-Sheng Cheng Department of Bioenvironmental Systems Engineering E-mail: rslab@ntu.edu.tw. Introduction.

By Lucy
(444 views)

## The Invisible Academy: nonlinear effects of linear learning

The Invisible Academy: nonlinear effects of linear learning. Mark Liberman University of Pennsylvania myl@cis.upenn.edu. Outline. An origin myth: naming without Adam a computer-assisted thought experiment

By yule
(193 views)

## Chapter 5: Continuous Random Variables

Chapter 5: Continuous Random Variables. Where We’ve Been. Using probability rules to find the probability of discrete events Examined probability models for discrete random variables. Where We’re Going. Develop the notion of a probability distribution for a continuous random variable

By annalise
(558 views)

## 5-1 Random Variables and Probability Distributions

5-1 Random Variables and Probability Distributions. The Binomial Distribution. Random Variables. Discrete – These variables take on a finite number of values, or a countable number of values Number of days absent Number of students taking a course

By britain
(117 views)

## Continuous random variables

Continuous random variables. Continuous random variable Let X be such a random variable Takes on values in the real space (-infinity; +infinity) (lower bound; upper bound) Instead of using P(X= i ) Use the probability density function f X (t) Or f X (t) dt.

By lis
(968 views)

## 4.2 (cont.) Expected Value of a Discrete Random Variable

4.2 (cont.) Expected Value of a Discrete Random Variable. A measure of the “middle” of the values of a random variable. Center. The mean of the probability distribution is the expected value of X, denoted E(X) E(X) is also denoted by the Greek letter µ (mu) . Economic Scenario. Profit

By feo
(289 views)

## CS498-EA Reasoning in AI Lecture #9

CS498-EA Reasoning in AI Lecture #9. Instructor: Eyal Amir Fall Semester 2011. Previously. First-Order Logic Syntax: Well-Founded Formulas Semantics: Models, Satisfaction, Entailment Models of FOL: how many, sometimes unexpected Resolution in FOL Resolution rule Unification Clausal form

By deo
(500 views)

## Variance and Covariance

Chapter 4.2. Variance and Covariance. Variance and Covariance. The mean or expected value of a random variable X is important because it describes the center of the probability distribution.

By talasi
(290 views)

## 4.7 Brownian Bridge

4.7 Brownian Bridge. 報告者 : 劉彥君. 4.7.1 Gaussian Process. Definition 4.7.1:

By vincent
(7183 views)

## DATA 220 Mathematical Methods for Data Analysis September 17 Class Meeting

DATA 220 Mathematical Methods for Data Analysis September 17 Class Meeting. Department of Applied Data Science San Jose State University Fall 2019 Instructor: Ron Mak www.cs.sjsu.edu/~mak. Some Counting Principles.

By ashley
(188 views)

## 45-733: lecture 6 (chapter 5)

45-733: lecture 6 (chapter 5). Continuous Random Variables. Joint continuous distributions. The joint continuous distribution is a complete probabilistic description of a group of r.v.s Describes each r.v. Describes the relationship among r.v.s. Joint continuous distributions.

By abe
(438 views)

## Review

Review. Lecture 42 Tue, Dec 12, 2006. Chapter 1. Sections 1.1 – 1.4. Be familiar with the language and principles of hypothesis testing. Given two explicit hypotheses, be able to calculate  and  . Given a value of the “test statistic,” be able to calculate the p -value. Etc.

By garran
(496 views)

## Chapter 5 Discrete Probability Distributions

Chapter 5 Discrete Probability Distributions. 5.3 EXPECTATION 5.3.1 The Mean and Expectation (Expected Value) 5.3.2 Some Applications 5.4 VARIANCE AND STANDARD DEVIATION. 5.3 EXPECTATION. 5.3.1 The Mean and Expectation (Expected Value) Experimental approach

By vidal
(141 views)

## 5.3 Martingale Representation Theorem

5.3 Martingale Representation Theorem. 報告者：顏妤芳. 5.3.1 Martingale Representation with One Brownian Motion. Corollary 5.3.2 is not a trivial consequence of the Martingale Representation Theorem , Theorem 5.3.1, with replacing W(t)

By caressa
(235 views)

## Selection --Medians and Order Statistics (Chap. 9)

Selection --Medians and Order Statistics (Chap. 9). The i th order statistic of n elements S={ a 1 , a 2 ,…, a n } : i th smallest elements Also called selection problem Minimum and maximum Median, lower median, upper median Selection in expected/average linear time

By chi
(242 views)

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