# 'Probability functions' presentation slideshows

## RANDOM VARIABLES

RANDOM VARIABLES. Random Variable. A random variable  or  stochastic variable  is a variable whose value is subject to variations due to chance.

By stamos
(84 views)

## LECTURE 02: BAYESIAN DECISION THEORY

LECTURE 02: BAYESIAN DECISION THEORY. Objectives: Bayes Rule Minimum Error Rate Decision Surfaces Gaussian Distributions Resources: D.H.S: Chapter 2 (Part 1) D.H.S: Chapter 2 (Part 2) R.G.O. : Intro to PR. Probability Decision Theory.

By lalo
(157 views)

## Probability

Precalculus. Lesson 9.3. Probability. Quick Review. What you’ll learn about. Sample Spaces and Probability Functions Determining Probabilities Venn Diagrams and Tree Diagrams Conditional Probability Binomial Distributions … and why Everyone should know how mathematical the “laws of

By tirzah
(124 views)

## Homework, Page 708

Homework, Page 708. Count the number of ways that each procedure can be done. 1. Line up three people for a photograph. Homework, Page 708. 5. There are four candidates for homecoming queen and three candidates for king. How many king-queen pairings are possible?. Homework, Page 708.

By natane
(170 views)

## LECTURE 02: BAYESIAN DECISION THEORY

LECTURE 02: BAYESIAN DECISION THEORY. Objectives: Bayes Rule Minimum Error Rate Decision Surfaces Resources: D.H.S: Chapter 2 (Part 1) D.H.S : Chapter 2 (Part 2) R.G.O. : Intro to PR. Audio:. URL:. Probability Decision Theory.

By watson
(219 views)

## Section 5 – Expectation and Other Distribution Parameters

Section 5 – Expectation and Other Distribution Parameters. Expected Value (mean). As the number of trials increases, the average outcome will tend towards E(X): the mean Expectatio n: Discrete Continuous. Expectation of h(x). Discrete Continuous. Moments of a Random Variable.

By nasnan
(78 views)

## Section 9 – Functions and Transformations of Random Variables

Section 9 – Functions and Transformations of Random Variables. Distribution of a transformation of continuous RV: X. Y = u(X) Y is defined as a function “u” of X v(u(x))=x Function “v” is defined as function the inverse function of “u” Obtain v(u(x)) by solving the given Y=u(x) for x.

By audra
(120 views)

## Information Fusion

Information Fusion. Yu Cai. Research Paper. Johan Schubert, “Clustering belief functions based on attracting and conflicting meta level evidence”, July 2002. Schubert Paper.

By latoya
(102 views)

## Maximum Likelihood Estimate

Maximum Likelihood Estimate. Jyh-Shing Roger Jang ( 張智星 ) CSIE Dept, National Taiwan University. Intro. to Maximum Likelihood Estimate. MLE Maximum likelihood estimate Goal:

By rachelh
(2 views)

## Random Variables

Random Variables. OBJECTIVE. Construct a probability distribution. Find measures of center and spread for a probability distribution. RELEVANCE. To find the likelihood of all possible outcomes of a probability distribution and to describe the distribution. Definition…….

By tbible
(1 views)

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