'Discrete uniform distribution' presentation slideshows

Chapter 5: Probability Distributions: Discrete Probability Distributions

Chapter 5: Probability Distributions: Discrete Probability Distributions. Learning Objectives. Identifying Types of Discrete Probability Distribution and their Respective Functional Representations.

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Chapter 5

Chapter 5. Some Discrete Probability Distributions. Chapter 5. Some Discrete Probability Distributions. Chapter 5.1. Introduction. Introduction. Often, the observations generated by different statistical experiments have the same general type of behavior.

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Optimal Multi-Period Asset Allocation for Life Insurance Policies

Optimal Multi-Period Asset Allocation for Life Insurance Policies. Hong-Chih Huang Associate Professor, Department of Risk Management and Insurance, National Chengchi University, Taiwan Yung-Tsung Lee

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Computational Intelligence Winter Term 2011/12

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Simulation Examples

Simulation Examples. Modeling and Simulation CS 313. EXAMPLE 2.1 Single-Channel Queue.

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Lecture 25 Simulation and Modeling

Lecture 25 Simulation and Modeling. Md. Tanvir Al Amin, Lecturer, Dept. of CSE, BUET. CSE 411. Recap : Inverse Transform Method. Find cumulative distribution function Let it be F Find F -1 X = F -1 (R) is the desired variate where R ~ U(0,1) Other methods are Accept Reject technique

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M obile C omputing G roup

M obile C omputing G roup. A quick-and-dirty tutorial on the chi2 test for goodness-of-fit testing. Outline. The presentation follows the pyramid schema. Chi2 tests for GoF. Goodness-of-fit (GoF). Background -concepts. Background. Descriptive vs. inferential statistics

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Random-Variate Generation

Random-Variate Generation. Need for Random-Variates. We, usually, model uncertainty and unpredictability with statistical distributions Thereby, in order to run the simulation models involving uncertainty, we need to get samples from these statistical distributions

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Lectures prepared by: Elchanan Mossel Yelena Shvets

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Lecture (10)

Lecture (10). Mathematical Expectation. Mathematical Expectation. The expected value of a variable is the value of a descriptor when averaged over a large number theoretically infinite. Mathematical Expectation (cont.). Another way to compute the variance. 0 1 2. TT TH HT HH.

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Random Variables

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Class notes for ISE 201 San Jose State University Industrial & Systems Engineering Dept.

Probability & Statistics for Engineers & Scientists, by Walpole, Myers, Myers & Ye ~ Chapter 5 Notes. Class notes for ISE 201 San Jose State University Industrial & Systems Engineering Dept. Steve Kennedy. Discrete Probability Distributions.

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Basics of probability

Basics of probability. Notes from: www.anu.edu.au/nceph/surfstat. A concept of probability. Probability is the study of uncertainty. E.g. A coin comes down heads 50% of the time. In the limit , as # tosses -> infinity . Example - 2 coin tosses.

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Probability Distributions

Probability Distributions. Chapter 7. The random variable X has a single value for each outcome within an experiment Discrete variables have separate and equi -distant values from each other Ex. 1, 2, 3, and 4 Continuous variables have an infinite number of possible values Ex. 1.2367

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Probability Distributions

Probability Distributions. ROCKS!!!!!!!!. Agenda. Probability Distributions Binomial Distributions Geometric Distributions Hypergeometric Distributions. Probability Distribution. The probability distribution consists of a table of values of X=x and P(x) or a graph.

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