PPT-Chapter 4 Continuous Random Variables and Probability Distributions

41 Probability Density Functions 42 Cumulative Distribution Func tions and Expected Values 43 The Normal Distribution 44 The Exponential and Gamma Distributions. Pieter . Abbeel. UC Berkeley EECS. Many slides adapted from . Thrun. , . Burgard. and Fox, Probabilistic Robotics. TexPoint fonts used in EMF. . Read the TexPoint manual before you delete this box.: . QSCI 381 – Lecture 12. (Larson and Farber, Sect 4.1). Learning objectives. Become comfortable with variable definitions. Create and use probability distributions. Random Variables-I. A . A Brief Introduction. Random Variables. Random Variable (RV): A numeric outcome that results from an experiment. For each element of an experiment’s sample space, the random variable can take on exactly one value. http://. rchsbowman.wordpress.com/2009/11/29. /. statistics-notes-%E2%80%93-properties-of-normal-distribution-2/. Chapter 23: Probability Density Functions. http://. divisbyzero.com/2009/12/02. /. an-applet-illustrating-a-continuous-nowhere-differentiable-function//. 1. Matt Gormley. Lecture 2. August 31, 2016. School of Computer Science. Readings:. Mitchell Ch. 1, 2, 6.1 – 6.3. Murphy Ch. 2. Bishop Ch. 1 - 2. 10-601 Introduction to Machine Learning. Reminders. Random Variables. Definition:. A rule that assigns one (and only one) numerical value to each simple event of an experiment; or. A function that assigns numerical values to the possible outcomes of an experiment.. . .. . . Week 05 . Tues. . .. MAT135 Statistics. Random Variables. A random variable . . Random Variables. A random variable . . “varies” . . (not always the same). Random Variables. A random variable . How . can it be that mathematics, being after all a product of human thought independent of experience, is so admirably adapted to the objects . of reality. Albert Einstein. Some parts of these slides were prepared based on . Random Variables. Definition:. A rule that assigns one (and only one) numerical value to each simple event of an experiment; or. A function that assigns numerical values to the possible outcomes of an experiment.. Section 5-3 – Normal Distributions: Finding Values. A. We have learned how to calculate the probability given an . x. -value or a . z. -score. . In this lesson, we will explore how to find an . Continuous Probability Distribution . (pdf) . Definition:. . b. P(a . . X.  . b) = .  . f(x). dx. . . a. For continuous RV X & a. .  b.. II. BINOMIAL DISTRIBUTIONS A. Binomial Experiments 1. A binomial experiment is a probability experiment that satisfies the following conditions: a. The experiment is repeated for a fixed number of independent trials. Objective. : . Use experimental and theoretical distributions to make judgments about . the . likelihood of various outcomes in uncertain . situations. CHS Statistics. Decide if the following random variable x is discrete(D) or continuous(C). . Section 6.1. Discrete and Continuous. Random Variables. Discrete and Continuous Random Variables. USE the probability distribution of a discrete random variable to CALCULATE the probability of an event..