'Discrete variable' diaporamas de présentation

Probabilistic Reasoning

Probabilistic Reasoning. Chapter 14 in the text book. Outline. Representing Knowledge in an Uncertain Domain The Semantics of Bayesian Networks Efficient Representation of Conditional Distributions Exact Inference in Bayesian Networks Approximate Inference in Bayesian Networks.

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Fourier Transformation

Fourier Transformation. Fourier Transformasjon. f(x). F( u ). Continuous Fourier Transform Def. The Fourier transform of a one-dimentional function f(x). The Inverse Fourier Transform. Continuous Fourier Transform Def - Notation. The Fourier transform

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Fourier Transformation

Fourier Transformation. Fourier Transformasjon. f(x). F( u ). Continuous Fourier Transform Def. The Fourier transform of a one-dimentional function f(x). The Inverse Fourier Transform. Continuous Fourier Transform Def - Notation. The Fourier transform

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Introduction to Categorical Data Analysis

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Time - Domain Analysis Of Discrete - Time Systems

The first half of Chapter 3. Time - Domain Analysis Of Discrete - Time Systems. 3.1. Introduction. 3.1-0 Discrete signals and systems. Discrete-Time Signal. A Sequence of numbers inherently Discrete-Time sampling Continuous-Time signals, ex) . Discrete-Time System.

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Discrete Probability Distributions

Discrete Probability Distributions. Random variables Discrete probability distributions Expected value and variance Binomial probability distribution. Random Variables. A random variable is a numerical description of the outcome of an experiment. Discrete Random Variables.

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Chapter 4

Chapter 4. 4-1. Discrete Probability Distributions. Outline. 4-2. 4-1 Introduction 4-2 Probability Distributions and Expectation 4-3 The Binomial Distribution 4-4 Mean, Variance and Standard Deviation for the Binomial Distribution. Objectives. 4-3.

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Exercise 19: Sample Size

Exercise 19: Sample Size. Part One. Explore how sample size affects the distribution of sample proportions This was achieved by first taking random samples 20 times when n=10 and then taking 20 random samples where n=40. These random samples were then summarized as sample statistics (p-hat). .

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7.2 Means and Variances of Random Variables

7.2 Means and Variances of Random Variables. Review. Review. Review. Review. Mean of a Discrete Random Variable.

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NURS/HSCI 597 NURSING RESEARCH & DATA ANALYSIS

NURS/HSCI 597 NURSING RESEARCH & DATA ANALYSIS. GEORGE MASON UNIVERSITY. OBJECTIVES. Discuss the nature, purpose, and types of statistics Discuss variables, levels of measurement, and their relationships to statistical analysis. Introduction to Data Analysis. Statistics:

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Descriptive Statistics: Part One

Descriptive Statistics: Part One. Farrokh Alemi Ph.D. Kashif Haqqi M.D. Objectives Definitions Sampling methods Types of variables. Reliability and validity Average Median Mode. Table of Content. Objectives.

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Modeling Discrete Variables

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The data display dance: How do you pick a partner who is singing the same tune?

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Descriptive Statistics

Descriptive Statistics. What a Data Set Tells Us. 15.1 Organizing and Visualizing Data Objectives:. Understand the difference between a sample and a population. Organize data in a frequency table. Use a variety of methods to represent data visually.

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Statistics: Descriptive and Inferential

Statistics: Descriptive and Inferential. Data is numerical. Statistics. A set of mathematical techniques used by social scientists to organize and manipulate data for the purpose of answering questions and testing theories. Variable. Any trait that can change values from case to case.

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Definition

Definition A random variable is a variable whose value is determined by the outcome of a random experiment/chance situation. Discrete Random Variable. Definition A random variable that assumes isolated, separated values is called a discrete random variable . THINK : Counts!!!.

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What Are We Summarizing?

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4th IMPRS Astronomy Summer School Drawing Astrophysical Inferences from Data Sets

4th IMPRS Astronomy Summer School Drawing Astrophysical Inferences from Data Sets. William H. Press The University of Texas at Austin Lecture 5. We want to go beyond simply maximizing and get the whole Bayesian posterior distribution of .

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