Chris Jernigan and Estelle Diener-Stroup (Can and Moore 2010)

Slides:



Advertisements
Similar presentations
Designing Investigations to Predict Probabilities Of Events.
Advertisements

Probability What are your Chances?
Probability Three basic types of probability: Probability as counting
Copyright © 2010, 2007, 2004 Pearson Education, Inc. Chapter 14 From Randomness to Probability.
A measurement of fairness game 1: A box contains 1red marble and 3 black marbles. Blindfolded, you select one marble. If you select the red marble, you.
Clear your desk for your quiz. Unit 2 Day 8 Expected Value Average expectation per game if the game is played many times Can be used to evaluate and.
Chapter 17 STA 200 Summer I Flipping Coins If you toss a coin repeatedly, you expect it to come up heads half the time. Suppose you toss a coin.
Random Variables. Definitions A random variable is a variable whose value is a numerical outcome of a random phenomenon,. A discrete random variable X.
Section 5.1 and 5.2 Probability
Take out a coin! You win 4 dollars for heads, and lose 2 dollars for tails.
Probability and Statistics1  Basic Probability  Binomial Distribution  Statistical Measures  Normal Distribution.
Learn to estimate probability using theoretical methods.
AP Statistics Section 6.2 A Probability Models
Probability And Expected Value ————————————
Random Variables A Random Variable assigns a numerical value to all possible outcomes of a random experiment We do not consider the actual events but we.
Expected Value.  In gambling on an uncertain future, knowing the odds is only part of the story!  Example: I flip a fair coin. If it lands HEADS, you.
Probability and Statistics Review
Slide 1 Statistics Workshop Tutorial 4 Probability Probability Distributions.
Section The Idea of Probability Statistics.
Copyright © 2010, 2007, 2004 Pearson Education, Inc. Chapter 14 From Randomness to Probability.
Chapter 16: Random Variables
Warm-up The mean grade on a standardized test is 88 with a standard deviation of 3.4. If the test scores are normally distributed, what is the probability.
CHAPTER 10: Introducing Probability
Stat 1510: Introducing Probability. Agenda 2  The Idea of Probability  Probability Models  Probability Rules  Finite and Discrete Probability Models.
Binomial Distributions Calculating the Probability of Success.
Probability Distributions - Discrete Random Variables Outcomes and Events.
Applied Business Forecasting and Regression Analysis Review lecture 2 Randomness and Probability.
Aim: How do we find the probability with two outcomes? Do Now: John takes a multiple choice test on a topic for which he has learned nothing. Each question.
Quiz Time! Clear your desk except for a pencil & calculator!
Outline Random processes Random variables Probability histograms
AP STATS: Take 10 minutes or so to complete your 7.1C quiz.
P. STATISTICS LESSON 8.2 ( DAY 1 )
Chapter 16: Random Variables
Chapter 7: Random Variables “Horse sense is what keeps horses from betting on what people do.” Damon Runyon.
Lecture PowerPoint Slides Basic Practice of Statistics 7 th Edition.
1 Since everything is a reflection of our minds, everything can be changed by our minds.
1 Chapter 9 Introducing Probability. From Exploration to Inference p. 150 in text Purpose: Unrestricted exploration & searching for patterns Purpose:
Some figures adapted from a 2004 Lecture by Larry Liebovitch, Ph.D. Chaos BIOL/CMSC 361: Emergence 1/29/08.
The Cosmic Ray Observatory Project High Energy Physics Group The University of Nebraska-Lincoln Counting Random Events A “fair” coin is flipped at the.
Expected Value.
CHAPTER 10: Introducing Probability ESSENTIAL STATISTICS Second Edition David S. Moore, William I. Notz, and Michael A. Fligner Lecture Presentation.
5.1 Probability in our Daily Lives.  Which of these list is a “random” list of results when flipping a fair coin 10 times?  A) T H T H T H T H T H 
1 Discrete Random Variables – Outline 1.Two kinds of random variables – Discrete – Continuous 2. Describing a DRV 3. Expected value of a DRV 4. Variance.
L56 – Discrete Random Variables, Distributions & Expected Values
Random Variables Learn how to characterize the pattern of the distribution of values that a random variable may have, and how to use the pattern to find.
Independent Events Lesson Starter State in writing whether each of these pairs of events are disjoint. Justify your answer. If the events.
1 ECE310 – Lecture 22 Random Signal Analysis 04/25/01.
1 Keep Life Simple! We live and work and dream, Each has his little scheme, Sometimes we laugh; sometimes we cry, And thus the days go by.
Binomial Distributions Chapter 5.3 – Probability Distributions and Predictions Mathematics of Data Management (Nelson) MDM 4U.
MATH 256 Probability and Random Processes Yrd. Doç. Dr. Didem Kivanc Tureli 14/10/2011Lecture 3 OKAN UNIVERSITY.
CHAPTER 10: Introducing Probability ESSENTIAL STATISTICS Second Edition David S. Moore, William I. Notz, and Michael A. Fligner Lecture Presentation.
© 2010 Pearson Education, Inc. All rights reserved Chapter 9 9 Probability.
Probability & Independence. Sample space Random variable Probability.
Binomial Distributions Chapter 5.3 – Probability Distributions and Predictions Mathematics of Data Management (Nelson) MDM 4U Authors: Gary Greer (with.
Section The Idea of Probability AP Statistics
Copyright © 2010 Pearson Education, Inc. Chapter 14 From Randomness to Probability.
CHAPTER 10: Introducing Probability
Discrete Random Variables
Chapter Randomness, Probability, and Simulation
Expected Value.
PROBABILITY The probability of an event is a value that describes the chance or likelihood that the event will happen or that the event will end with.
Daniela Stan Raicu School of CTI, DePaul University
Chapter 17 Thinking about Chance.
Probability Union Intersection Complement
Econometric Models The most basic econometric model consists of a relationship between two variables which is disturbed by a random error. We need to use.
Expected Value.
CHAPTER 10: Introducing Probability
Random Variable Random Variable – Numerical Result Determined by the Outcome of a Probability Experiment. Ex1: Roll a Die X = # of Spots X | 1.
From Randomness to Probability
Expected Value.
Presentation transcript:

Chris Jernigan and Estelle Diener-Stroup (Can and Moore 2010)

 Chaos in space  Infinitely detailed line or object.....  Can you find the exact area of the shaded region? (Baranger 2010)

 ons/6/6d/Animated_fractal_mountain.gif ons/6/6d/Animated_fractal_mountain.gif  ons/f/fd/Von_Koch_curve.gif ons/f/fd/Von_Koch_curve.gif  4/Animated_construction_of_Sierpinski_Tria ngle.gif 4/Animated_construction_of_Sierpinski_Tria ngle.gif

 Fractal data set ▪ Cannot be described by mean or variance (Liebovich and Scheurle 2000)

 Data distribution with increasing amounts of new data (Liebovich and Scheurle 2000)

 Normal Coin Toss  Tails Win nothing, Head Win $1 ▪ (1/2)*1 + (1/2)*0 = $0.5  On average you should win $0.5, so could fairly gamble $1 (Liebovich and Scheurle 2000)

 St. Petersburg Coin Toss Game  Flip a coin until it lands on heads  Lands: heads = $2; tails, heads = $4; tails, tails, heads = $8 ▪ (1/2)*2+ (1/4)*4+(1/8)*8.... = = ∞  Half the time you win at least $2 so could fairly wager $4, however casino will correctly argue that the mean winnings per game is infinite and therefore should put up more than all the money in the universe to play the game (Liebovich and Scheurle 2000)

 The probability that any measurement has a value between x and x+d(x).... The PDF of the times between episodes of the onset of rapid heart rate measured in patients with implanted cardioverter defibrillators from the work of Liebovitch et al. [1]. Most often the time between episodes is brief. Less often the time is longer. Infrequently it is very long. There is no single average time that characterizes the times between these events. The PDF has a power law form that is a straight line on a plot of log[PDF(t)] versus log(t) (Liebovich and Scheurle 2000)

 “Even when events occur at random, they are often bunched together and the bunches have bunches which have bunches......”  “One purpose of studying chaos though fractals is to predict patterns in dynamical systems that on the surface seem unpredictable” (Liebovich and Scheurle 2000) (Presley 2010)

 Baranger, M Chaos, Complexity, and Entropy: A physics talk for non-physicists. MIT..  Can, T. And Moore, W. Fractals and Chaos in the Driven Pendulum: A Review and Numerical Study of a Strange Attractor  Liebovich, L.S. and Scheurle D Two Lessons from Fractals and Chaos: Changes in the way we see the world. Complexity 5(4). John Wiley & Sons, Inc  Presley, R.E Fractals in Nature..