'Discrete distributions' presentation slideshows

Chapter 4

Chapters 1. Introduction 2. Graphs 3. Descriptive statistics 4. Basic probability 5. Discrete distributions 6. Continuous distributions 7. Central limit theorem 8. Estimation 9. Hypothesis testing 10. Two-sample tests 13. Linear regression

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Dr. Imad Khamis

Dr. Imad Khamis. Probability: The Study of Randomness. OBJECTIVES. To define the term probability. To discuss the classical, the relative frequency, and the subjective approaches to probability. To understand the terms experiment, event, and outcome.

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Chapter 11: Selected Quantitative Relationships (pt. 1)

Chapter 11: Selected Quantitative Relationships (pt. 1). ISE 443 / ETM 543 Fall 2013. The development of large, complex systems involves the use of quantitative relationships. To predict system performance before it is built response times success rates system availability

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A Refresher on Probability and Statistics

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A Refresher on Probability and Statistics

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A Refresher on Probability and Statistics

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Laboratory in Oceanography: Data and Methods

Laboratory in Oceanography: Data and Methods. Intro to the Statistics Toolbox. MAR550, Spring 2013 Miles A. Sundermeyer. Intro to Statistics Toolbox Statistics Toolbox/Descriptive Statistics. Measures of Central Tendency Geometric Mean: Harmonic Mean:. Intro to Statistics Toolbox

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A new bound for discrete distributions based on Maximum Entropy

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Generating Functions

Generating Functions. The Moments of Y. We have referred to E(Y) and E(Y 2 ) as the first and second moments of Y, respectively. In general, E(Y k ) is the k th moment of Y. Consider the polynomial where the moments of Y are incorporated into the coefficients. Moment Generating Function.

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Generating Functions

Generating Functions. The Moments of Y. We have referred to E(Y) and E(Y 2 ) as the first and second moments of Y, respectively. In general, E(Y k ) is the k th moment of Y. Consider the polynomial where the moments of Y are incorporated into the coefficients. Moment Generating Function.

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Perfect Sampling using MCMC and its application in Signal P rocessing

Perfect Sampling using MCMC and its application in Signal P rocessing. Presented by Sridivya Gajjarapu Florida State University sg12k@my.fsu.edu. Overview. Perfect Sampling Coupling from the Past technique Gibbs coupler Applications in signal processing. Perfect sampling.

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Discrete Distributions

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Statistics 1: Elementary Statistics

Statistics 1: Elementary Statistics. Section 5-2. Discrete Probability Distributions. Quantitative data Discrete values List of outcomes together with their respective probabilities Counts are most common. Discrete Probability Distributions. Rules Probabilities sum to 1

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LECTURE 13: REESTIMATION, EM AND MIXTURES. Objectives: Reestimation Equations Continuous Distributions Gaussian Mixture Models EM Derivation of Reestimation

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Overall agenda

Overall agenda. Part 1 and 2. Part 1: Basic statistical concepts and descriptive statistics summarizing and visualising data describing data measures of location and variation exploring data types of data population and sample Part 2: Statistical Distributions concepts

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Discrete Distributions

Discrete Distributions. X=0:10; Y= geopdf (X,0.5); bar(X,Y,0.8) X=0:12; Y= geopdf (X,0.3); bar(X,Y,0.6) X=0:10; Y= binopdf (X,10,0.6); bar(X,Y,0.8) X=0:12; Y= binopdf (X,12,0.5); bar(X,Y,0.6) X=0:10; Y= poisspdf (X,4); bar(X,Y,0.8)

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