Uncertainty and Safety Measures
|
|
- Alexandra Paul
- 5 years ago
- Views:
Transcription
1 Uncertainty and Safety Measures How do we classify uncertainties? What are their sources? Lack of knowledge vs. variability. What type of safety measures do we take? Design, manufacturing, operations & post- mortems Living with uncertainties vs. changing g them How do we represent random variables? Probability distributions and moments
2 Reading assignment Oberkmapf et al. Error and uncertainty in modeling and simulation, Reliability Engineering and System Safety, 75, , 2002 Source: Page11.htm
3 Modeling uncertainty (Oberkampf et al.).
4 . Modeling stages
5 Classification of uncertainties Aleatory uncertainty: t Inherent variability Example: What does regular unleaded cost in Gainesville today? Epistemic uncertainty Lack of knowledge Source: Example: What will be the average cost of regular unleaded January 1, 2012? Distinction is not absolute Knowledge often reduces variability Example: Gas station A averages 5 cents more than city average while Gas station B 2 cents less. Scatter reduced when measured from station average!
6 A slightly different uncertainty t British Airways classification. Distinction between Acknowledged and Unacknowledged errors
7 Safety measures Design: Conservative loads and material properties, accurate models, certification of design Manufacture: Quality control, oversight by regulatory agency Operation: Licensing of operators, maintenance and inspections Post-mortem: o te Accident investigations s
8 Many players reduce uncertainty in aircraft structures. t The federal government (e.g. NASA) invests in developing more accurate models and measurement techniques. Boeing invests in higher fidelity simulations and high accuracy manufacturing. Airlines invest in maintenance and inspections. FAA invests in certification of aircraft & pilots. NTSB, FAA and NASA fund accident investigations.
9 Representation of uncertainty Random variables: Variables that can take multiple values with probability assigned to each value Representation of random variables Probability distribution function (PDF) Cumulative distribution function (CDF) Moments: Mean, variance, standard deviation, coefficient of variance (COV)
10 Histograms and PDF How do you estimate the PDF from a histogram? SOURCE: 611/akerley/question.jpg P robability distribution function f : P( a x a da) f ( x) da
11 Cumulative distribution function x Integral of PDF F( x) P( X x) f( t) dt X = [-3:0.1:3]; p=normcdf(x,0,1) plot(x,p) CDF x
12 Some STATISTICS Mean ( X ) xf ( xdx ) Variance 2 Var ( X ) ( x ) f ( x ) dx Standard deviation Var ( X ) Coefficient of variation COV
13 Uncertainty ypropagationp A function r(x) will have its own PDF
14 problems 1. List at least six safety measures or uncertainty reduction mechanisms used to reduce highway fatalities of automobile drivers. Source: Smithsonian Institution Number: Give examples of aleatory and epistemic uncertainty faced by car designers who want to ensure the safety of drivers. 3. Let x be a standard normal variable N(0,1). 3. Let x be a standard normal variable N(0,1). Calculate the mean and standard deviation of sin(x)
STATISTICS and PROBABILITY
Introduction to Statistics Atatürk University STATISTICS and PROBABILITY LECTURE: PROBABILITY DISTRIBUTIONS Prof. Dr. İrfan KAYMAZ Atatürk University Engineering Faculty Department of Mechanical Engineering
More informationTutorial 6. Sampling Distribution. ENGG2450A Tutors. 27 February The Chinese University of Hong Kong 1/6
Tutorial 6 Sampling Distribution ENGG2450A Tutors The Chinese University of Hong Kong 27 February 2017 1/6 Random Sample and Sampling Distribution 2/6 Random sample Consider a random variable X with distribution
More informationStatistics & Flood Frequency Chapter 3. Dr. Philip B. Bedient
Statistics & Flood Frequency Chapter 3 Dr. Philip B. Bedient Predicting FLOODS Flood Frequency Analysis n Statistical Methods to evaluate probability exceeding a particular outcome - P (X >20,000 cfs)
More informationRandom Variables and Probability Distributions
Chapter 3 Random Variables and Probability Distributions Chapter Three Random Variables and Probability Distributions 3. Introduction An event is defined as the possible outcome of an experiment. In engineering
More informationStatistics I Final Exam, 24 June Degrees in ADE, DER-ADE, ADE-INF, FICO, ECO, ECO-DER.
Statistics I Final Exam, June. Degrees in ADE, DER-ADE, ADE-INF, FICO, ECO, ECO-DER. EXAM RULES: Use separate booklets for each problem. Perform the calculations with at least two significant decimal places.
More informationNovember 2000 Course 1. Society of Actuaries/Casualty Actuarial Society
November 2000 Course 1 Society of Actuaries/Casualty Actuarial Society 1. A recent study indicates that the annual cost of maintaining and repairing a car in a town in Ontario averages 200 with a variance
More informationChapter 8: The Binomial and Geometric Distributions
Chapter 8: The Binomial and Geometric Distributions 8.1 Binomial Distributions 8.2 Geometric Distributions 1 Let me begin with an example My best friends from Kent School had three daughters. What is the
More informationPresented at the 2012 SCEA/ISPA Joint Annual Conference and Training Workshop -
Applying the Pareto Principle to Distribution Assignment in Cost Risk and Uncertainty Analysis James Glenn, Computer Sciences Corporation Christian Smart, Missile Defense Agency Hetal Patel, Missile Defense
More informationChapter 2. Random variables. 2.3 Expectation
Random processes - Chapter 2. Random variables 1 Random processes Chapter 2. Random variables 2.3 Expectation 2.3 Expectation Random processes - Chapter 2. Random variables 2 Among the parameters representing
More informationImportance Sampling for Option Pricing. Steven R. Dunbar. Put Options. Monte Carlo Method. Importance. Sampling. Examples.
for for January 25, 2016 1 / 26 Outline for 1 2 3 4 2 / 26 Put Option for A put option is the right to sell an asset at an established price at a certain time. The established price is the strike price,
More informationName: CS3130: Probability and Statistics for Engineers Practice Final Exam Instructions: You may use any notes that you like, but no calculators or computers are allowed. Be sure to show all of your work.
More informationLecture 5: Fundamentals of Statistical Analysis and Distributions Derived from Normal Distributions
Lecture 5: Fundamentals of Statistical Analysis and Distributions Derived from Normal Distributions ELE 525: Random Processes in Information Systems Hisashi Kobayashi Department of Electrical Engineering
More informationA Model of Coverage Probability under Shadow Fading
A Model of Coverage Probability under Shadow Fading Kenneth L. Clarkson John D. Hobby August 25, 23 Abstract We give a simple analytic model of coverage probability for CDMA cellular phone systems under
More informationA random variable X is a function that assigns (real) numbers to the elements of the sample space S of a random experiment.
RANDOM VARIABLES and PROBABILITY DISTRIBUTIONS A random variable X is a function that assigns (real) numbers to the elements of the samle sace S of a random exeriment. The value sace V of a random variable
More informationChapter 2: Random Variables (Cont d)
Chapter : Random Variables (Cont d) Section.4: The Variance of a Random Variable Problem (1): Suppose that the random variable X takes the values, 1, 4, and 6 with probability values 1/, 1/6, 1/, and 1/6,
More informationChapter 7 1. Random Variables
Chapter 7 1 Random Variables random variable numerical variable whose value depends on the outcome of a chance experiment - discrete if its possible values are isolated points on a number line - continuous
More informationNormal Cumulative Distribution Function (CDF)
The Normal Model 2 Solutions COR1-GB.1305 Statistics and Data Analysis Normal Cumulative Distribution Function (CDF 1. Suppose that an automobile muffler is designed so that its lifetime (in months is
More informationDeveloping Survey Expansion Factors
Developing Survey Expansion Factors Objective: To apply expansion factors to the results of a household travel survey and to apply trip rates to calculate total trips. It is eighteen months later and the
More informationTwo Hours. Mathematical formula books and statistical tables are to be provided THE UNIVERSITY OF MANCHESTER. 22 January :00 16:00
Two Hours MATH38191 Mathematical formula books and statistical tables are to be provided THE UNIVERSITY OF MANCHESTER STATISTICAL MODELLING IN FINANCE 22 January 2015 14:00 16:00 Answer ALL TWO questions
More informationELEMENTS OF MONTE CARLO SIMULATION
APPENDIX B ELEMENTS OF MONTE CARLO SIMULATION B. GENERAL CONCEPT The basic idea of Monte Carlo simulation is to create a series of experimental samples using a random number sequence. According to the
More informationECE 340 Probabilistic Methods in Engineering M/W 3-4:15. Lecture 10: Continuous RV Families. Prof. Vince Calhoun
ECE 340 Probabilistic Methods in Engineering M/W 3-4:15 Lecture 10: Continuous RV Families Prof. Vince Calhoun 1 Reading This class: Section 4.4-4.5 Next class: Section 4.6-4.7 2 Homework 3.9, 3.49, 4.5,
More informationBasic notions of probability theory: continuous probability distributions. Piero Baraldi
Basic notions of probability theory: continuous probability distributions Piero Baraldi Probability distributions for reliability, safety and risk analysis: discrete probability distributions continuous
More informationStatistics Chapter 8
Statistics Chapter 8 Binomial & Geometric Distributions Time: 1.5 + weeks Activity: A Gaggle of Girls The Ferrells have 3 children: Jennifer, Jessica, and Jaclyn. If we assume that a couple is equally
More informationcontinuous rv Note for a legitimate pdf, we have f (x) 0 and f (x)dx = 1. For a continuous rv, P(X = c) = c f (x)dx = 0, hence
continuous rv Let X be a continuous rv. Then a probability distribution or probability density function (pdf) of X is a function f(x) such that for any two numbers a and b with a b, P(a X b) = b a f (x)dx.
More information8.1 Binomial Distributions
8.1 Binomial Distributions The Binomial Setting The 4 Conditions of a Binomial Setting: 1.Each observation falls into 1 of 2 categories ( success or fail ) 2 2.There is a fixed # n of observations. 3.All
More informationProbability and Statistics
Kristel Van Steen, PhD 2 Montefiore Institute - Systems and Modeling GIGA - Bioinformatics ULg kristel.vansteen@ulg.ac.be CHAPTER 3: PARAMETRIC FAMILIES OF UNIVARIATE DISTRIBUTIONS 1 Why do we need distributions?
More informationBinomial Distributions
Binomial Distributions (aka Bernouli s Trials) Chapter 8 Binomial Distribution an important class of probability distributions, which occur under the following Binomial Setting (1) There is a number n
More informationAP Statistics Ch 8 The Binomial and Geometric Distributions
Ch 8.1 The Binomial Distributions The Binomial Setting A situation where these four conditions are satisfied is called a binomial setting. 1. Each observation falls into one of just two categories, which
More informationVersion A. Problem 1. Let X be the continuous random variable defined by the following pdf: 1 x/2 when 0 x 2, f(x) = 0 otherwise.
Math 224 Q Exam 3A Fall 217 Tues Dec 12 Version A Problem 1. Let X be the continuous random variable defined by the following pdf: { 1 x/2 when x 2, f(x) otherwise. (a) Compute the mean µ E[X]. E[X] x
More informationSTUDY SET 1. Discrete Probability Distributions. x P(x) and x = 6.
STUDY SET 1 Discrete Probability Distributions 1. Consider the following probability distribution function. Compute the mean and standard deviation of. x 0 1 2 3 4 5 6 7 P(x) 0.05 0.16 0.19 0.24 0.18 0.11
More informationb) Consider the sample space S = {1, 2, 3}. Suppose that P({1, 2}) = 0.5 and P({2, 3}) = 0.7. Is P a valid probability measure? Justify your answer.
JARAMOGI OGINGA ODINGA UNIVERSITY OF SCIENCE AND TECHNOLOGY BACHELOR OF SCIENCE -ACTUARIAL SCIENCE YEAR ONE SEMESTER ONE SAS 103: INTRODUCTION TO PROBABILITY THEORY Instructions: Answer question 1 and
More informationName: Date: Pd: Review 8.3 & 8.4
Name: Date: Pd: Review 8.3 & 8.4 Multiple Choice Identify the choice that best completes the statement or answers the question. 1. The histograms represent the probability distributions of the random variables
More informationINDIAN INSTITUTE OF SCIENCE STOCHASTIC HYDROLOGY. Lecture -5 Course Instructor : Prof. P. P. MUJUMDAR Department of Civil Engg., IISc.
INDIAN INSTITUTE OF SCIENCE STOCHASTIC HYDROLOGY Lecture -5 Course Instructor : Prof. P. P. MUJUMDAR Department of Civil Engg., IISc. Summary of the previous lecture Moments of a distribubon Measures of
More informationKing Saud University Academic Year (G) College of Sciences Academic Year (H) Solutions of Homework 1 : Selected problems P exam
King Saud University Academic Year (G) 6 7 College of Sciences Academic Year (H) 437 438 Mathematics Department Bachelor AFM: M. Eddahbi Solutions of Homework : Selected problems P exam Problem : An auto
More informationWeb Appendix. Are the effects of monetary policy shocks big or small? Olivier Coibion
Web Appendix Are the effects of monetary policy shocks big or small? Olivier Coibion Appendix 1: Description of the Model-Averaging Procedure This section describes the model-averaging procedure used in
More informationUQ, STAT2201, 2017, Lectures 3 and 4 Unit 3 Probability Distributions.
UQ, STAT2201, 2017, Lectures 3 and 4 Unit 3 Probability Distributions. Random Variables 2 A random variable X is a numerical (integer, real, complex, vector etc.) summary of the outcome of the random experiment.
More informationProblem # 2. In a country with a large population, the number of persons, N, that are HIV positive at time t is given by:
Problem # 1 A marketing survey indicates that 60% of the population owns an automobile, 30% owns a house, and 20% owns both an automobile and a house. Calculate the probability that a person chosen at
More informationLoss Simulation Model Testing and Enhancement
Loss Simulation Model Testing and Enhancement Casualty Loss Reserve Seminar By Kailan Shang Sept. 2011 Agenda Research Overview Model Testing Real Data Model Enhancement Further Development Enterprise
More informationSection 7.1: Continuous Random Variables
Section 71: Continuous Random Variables Discrete-Event Simulation: A First Course c 2006 Pearson Ed, Inc 0-13-142917-5 Discrete-Event Simulation: A First Course Section 71: Continuous Random Variables
More informationFinancial Econometrics Jeffrey R. Russell Midterm 2014
Name: Financial Econometrics Jeffrey R. Russell Midterm 2014 You have 2 hours to complete the exam. Use can use a calculator and one side of an 8.5x11 cheat sheet. Try to fit all your work in the space
More informationSTRESS-STRENGTH RELIABILITY ESTIMATION
CHAPTER 5 STRESS-STRENGTH RELIABILITY ESTIMATION 5. Introduction There are appliances (every physical component possess an inherent strength) which survive due to their strength. These appliances receive
More informationThe Binomial Probability Distribution
The Binomial Probability Distribution MATH 130, Elements of Statistics I J. Robert Buchanan Department of Mathematics Fall 2017 Objectives After this lesson we will be able to: determine whether a probability
More informationAdvanced Financial Economics Homework 2 Due on April 14th before class
Advanced Financial Economics Homework 2 Due on April 14th before class March 30, 2015 1. (20 points) An agent has Y 0 = 1 to invest. On the market two financial assets exist. The first one is riskless.
More informationThis homework assignment uses the material on pages ( A moving average ).
Module 2: Time series concepts HW Homework assignment: equally weighted moving average This homework assignment uses the material on pages 14-15 ( A moving average ). 2 Let Y t = 1/5 ( t + t-1 + t-2 +
More informationMultiple-Choice Questions
AP Statistics Testbank 6 Formulas: If,, 1 2..., n are random variables, then E ( 1 + 2 + + n ) = E( 1 ) + E( 2 ) + + E( n ). If,, 1 2..., n independent random variables, then Var + + + ) = Var( ) + Var(
More informationDependence Modeling and Credit Risk
Dependence Modeling and Credit Risk Paola Mosconi Banca IMI Bocconi University, 20/04/2015 Paola Mosconi Lecture 6 1 / 53 Disclaimer The opinion expressed here are solely those of the author and do not
More informationAnti-Trust Notice. The Casualty Actuarial Society is committed to adhering strictly
Anti-Trust Notice The Casualty Actuarial Society is committed to adhering strictly to the letter and spirit of the antitrust laws. Seminars conducted under the auspices of the CAS are designed solely to
More informationA. 11 B. 15 C. 19 D. 23 E. 27. Solution. Let us write s for the policy year. Then the mortality rate during year s is q 30+s 1.
Solutions to the Spring 213 Course MLC Examination by Krzysztof Ostaszewski, http://wwwkrzysionet, krzysio@krzysionet Copyright 213 by Krzysztof Ostaszewski All rights reserved No reproduction in any form
More information4-2 Probability Distributions and Probability Density Functions. Figure 4-2 Probability determined from the area under f(x).
4-2 Probability Distributions and Probability Density Functions Figure 4-2 Probability determined from the area under f(x). 4-2 Probability Distributions and Probability Density Functions Definition 4-2
More informationDiscrete Random Variables
Discrete Random Variables ST 370 A random variable is a numerical value associated with the outcome of an experiment. Discrete random variable When we can enumerate the possible values of the variable
More informationHeterogeneous Risks and GLM Extensions
Heterogeneous Risks and GLM Extensions CAS Annual Meeting, New York, Nov. 2014 Luyang Fu, FCAS, Ph.D. The author s affiliation with The Cincinnati Insurance Company is provided for identification purposes
More informationEDUCATION AND EXAMINATION COMMITTEE OF THE SOCIETY OF ACTUARIES RISK AND INSURANCE. Judy Feldman Anderson, FSA and Robert L.
EDUCATION AND EAMINATION COMMITTEE OF THE SOCIET OF ACTUARIES RISK AND INSURANCE by Judy Feldman Anderson, FSA and Robert L. Brown, FSA Copyright 2005 by the Society of Actuaries The Education and Examination
More informationWCB-Alberta. Premium Rate Guide
WCB-Alberta Premium Rate Guide Rate setting overview Employers pay premiums to fund workers compensation insurance. WCB-Alberta (WCB) determines premium requirements annually based on the best estimates
More informationPricing and risk of financial products
and risk of financial products Prof. Dr. Christian Weiß Riga, 27.02.2018 Observations AAA bonds are typically regarded as risk-free investment. Only examples: Government bonds of Australia, Canada, Denmark,
More informationMeasure of Variation
Measure of Variation Variation is the spread of a data set. The simplest measure is the range. Range the difference between the maximum and minimum data entries in the set. To find the range, the data
More informationValue at Risk Ch.12. PAK Study Manual
Value at Risk Ch.12 Related Learning Objectives 3a) Apply and construct risk metrics to quantify major types of risk exposure such as market risk, credit risk, liquidity risk, regulatory risk etc., and
More informationCan we use kernel smoothing to estimate Value at Risk and Tail Value at Risk?
Can we use kernel smoothing to estimate Value at Risk and Tail Value at Risk? Ramon Alemany, Catalina Bolancé and Montserrat Guillén Riskcenter - IREA Universitat de Barcelona http://www.ub.edu/riskcenter
More informationProbability and Statistics
Probability and Statistics Alvin Lin Probability and Statistics: January 2017 - May 2017 Binomial Random Variables There are two balls marked S and F in a basket. Select a ball 3 times with replacement.
More informationCHAPTER 1. Find the mean, median and mode for the number of returns prepared by each accountant.
CHAPTER 1 TUTORIAL 1. Explain the term below : i. Statistics ii. Population iii. Sample 2. A questionnaire provides 58 Yes, 42 No and 20 no-opinion. i. In the construction of a pie chart, how many degrees
More informationThe histogram should resemble the uniform density, the mean should be close to 0.5, and the standard deviation should be close to 1/ 12 =
Chapter 19 Monte Carlo Valuation Question 19.1 The histogram should resemble the uniform density, the mean should be close to.5, and the standard deviation should be close to 1/ 1 =.887. Question 19. The
More informationLecture Stat 302 Introduction to Probability - Slides 15
Lecture Stat 30 Introduction to Probability - Slides 15 AD March 010 AD () March 010 1 / 18 Continuous Random Variable Let X a (real-valued) continuous r.v.. It is characterized by its pdf f : R! [0, )
More informationHomework Problems In each of the following situations, X is a count. Does X have a binomial distribution? Explain. 1. You observe the gender of the next 40 children born in a hospital. X is the number
More information3. Monte Carlo Simulation
3. Monte Carlo Simulation 3.7 Variance Reduction Techniques Math443 W08, HM Zhu Variance Reduction Procedures (Chap 4.5., 4.5.3, Brandimarte) Usually, a very large value of M is needed to estimate V with
More informationBusiness Statistics 41000: Probability 3
Business Statistics 41000: Probability 3 Drew D. Creal University of Chicago, Booth School of Business February 7 and 8, 2014 1 Class information Drew D. Creal Email: dcreal@chicagobooth.edu Office: 404
More informationReview of the Topics for Midterm I
Review of the Topics for Midterm I STA 100 Lecture 9 I. Introduction The objective of statistics is to make inferences about a population based on information contained in a sample. A population is the
More information3. The n observations are independent. Knowing the result of one observation tells you nothing about the other observations.
Binomial and Geometric Distributions - Terms and Formulas Binomial Experiments - experiments having all four conditions: 1. Each observation falls into one of two categories we call them success or failure.
More information***SECTION 8.1*** The Binomial Distributions
***SECTION 8.1*** The Binomial Distributions CHAPTER 8 ~ The Binomial and Geometric Distributions In practice, we frequently encounter random phenomenon where there are two outcomes of interest. For example,
More informationFinancial Time Series and Their Characteristics
Financial Time Series and Their Characteristics Egon Zakrajšek Division of Monetary Affairs Federal Reserve Board Summer School in Financial Mathematics Faculty of Mathematics & Physics University of Ljubljana
More informationChapter 8.1.notebook. December 12, Jan 17 7:08 PM. Jan 17 7:10 PM. Jan 17 7:17 PM. Pop Quiz Results. Chapter 8 Section 8.1 Binomial Distribution
Chapter 8 Section 8.1 Binomial Distribution Target: The student will know what the 4 characteristics are of a binomial distribution and understand how to use them to identify a binomial setting. Process
More informationMS&E 448 Final Presentation High Frequency Algorithmic Trading
MS&E 448 Final Presentation High Frequency Algorithmic Trading Francis Choi George Preudhomme Nopphon Siranart Roger Song Daniel Wright Stanford University June 6, 2017 High-Frequency Trading MS&E448 June
More informationMuch of what appears here comes from ideas presented in the book:
Chapter 11 Robust statistical methods Much of what appears here comes from ideas presented in the book: Huber, Peter J. (1981), Robust statistics, John Wiley & Sons (New York; Chichester). There are many
More informationSIMULATION OF ELECTRICITY MARKETS
SIMULATION OF ELECTRICITY MARKETS MONTE CARLO METHODS Lectures 15-18 in EG2050 System Planning Mikael Amelin 1 COURSE OBJECTIVES To pass the course, the students should show that they are able to - apply
More informationLesson Plan for Simulation with Spreadsheets (8/31/11 & 9/7/11)
Jeremy Tejada ISE 441 - Introduction to Simulation Learning Outcomes: Lesson Plan for Simulation with Spreadsheets (8/31/11 & 9/7/11) 1. Students will be able to list and define the different components
More informationFundamentals of Statistics
CHAPTER 4 Fundamentals of Statistics Expected Outcomes Know the difference between a variable and an attribute. Perform mathematical calculations to the correct number of significant figures. Construct
More information3. The n observations are independent. Knowing the result of one observation tells you nothing about the other observations.
Binomial and Geometric Distributions - Terms and Formulas Binomial Experiments - experiments having all four conditions: 1. Each observation falls into one of two categories we call them success or failure.
More informationMath489/889 Stochastic Processes and Advanced Mathematical Finance Homework 5
Math489/889 Stochastic Processes and Advanced Mathematical Finance Homework 5 Steve Dunbar Due Fri, October 9, 7. Calculate the m.g.f. of the random variable with uniform distribution on [, ] and then
More informationRandom variables. Contents
Random variables Contents 1 Random Variable 2 1.1 Discrete Random Variable............................ 3 1.2 Continuous Random Variable........................... 5 1.3 Measures of Location...............................
More informationModule 3: Sampling Distributions and the CLT Statistics (OA3102)
Module 3: Sampling Distributions and the CLT Statistics (OA3102) Professor Ron Fricker Naval Postgraduate School Monterey, California Reading assignment: WM&S chpt 7.1-7.3, 7.5 Revision: 1-12 1 Goals for
More informationGamma. The finite-difference formula for gamma is
Gamma The finite-difference formula for gamma is [ P (S + ɛ) 2 P (S) + P (S ɛ) e rτ E ɛ 2 ]. For a correlation option with multiple underlying assets, the finite-difference formula for the cross gammas
More informationLecture Note 8 of Bus 41202, Spring 2017: Stochastic Diffusion Equation & Option Pricing
Lecture Note 8 of Bus 41202, Spring 2017: Stochastic Diffusion Equation & Option Pricing We shall go over this note quickly due to time constraints. Key concept: Ito s lemma Stock Options: A contract giving
More informationTHE AUSTRALIAN JOURNAL OF
THE AUSTRALIAN JOURNAL OF AGRICULTURAL ECONOMICS VOL. 13 DECEMBER 1969 NO. 2 A SIMULATED STUDY OF AN AUCTION MARKET R. B. WHAN and R. A. RICHARDSON* Bureau of Agricultural Economics A simulated model of
More informationStatistical analysis and bootstrapping
Statistical analysis and bootstrapping p. 1/15 Statistical analysis and bootstrapping Michel Bierlaire michel.bierlaire@epfl.ch Transport and Mobility Laboratory Statistical analysis and bootstrapping
More informationII. Random Variables
II. Random Variables Random variables operate in much the same way as the outcomes or events in some arbitrary sample space the distinction is that random variables are simply outcomes that are represented
More informationPortfolio Risk Management and Linear Factor Models
Chapter 9 Portfolio Risk Management and Linear Factor Models 9.1 Portfolio Risk Measures There are many quantities introduced over the years to measure the level of risk that a portfolio carries, and each
More informationName Period AP Statistics Unit 5 Review
Name Period AP Statistics Unit 5 Review Multiple Choice 1. Jay Olshansky from the University of Chicago was quoted in Chance News as arguing that for the average life expectancy to reach 100, 18% of people
More informationOn-line Appendix: The Mutual Fund Holdings Database
Unexploited Gains from International Diversification: Patterns of Portfolio Holdings around the World Tatiana Didier, Roberto Rigobon, and Sergio L. Schmukler Review of Economics and Statistics, forthcoming
More information2) There is a fixed number of observations n. 3) The n observations are all independent
Chapter 8 Binomial and Geometric Distributions The binomial setting consists of the following 4 characteristics: 1) Each observation falls into one of two categories success or failure 2) There is a fixed
More informationSection 8.2: Monte Carlo Estimation
Section 8.2: Monte Carlo Estimation Discrete-Event Simulation: A First Course c 2006 Pearson Ed., Inc. 0-13-142917-5 Discrete-Event Simulation: A First Course Section 8.2: Monte Carlo Estimation 1/ 19
More informationProbability and Statistics for Engineers
Probability and Statistics for Engineers Chapter 4 Probability Distributions ruochen Liu ruochenliu@xidian.edu.cn Institute of Intelligent Information Processing, Xidian University Outline Random variables
More informationMonte Carlo Introduction
Monte Carlo Introduction Probability Based Modeling Concepts moneytree.com Toll free 1.877.421.9815 1 What is Monte Carlo? Monte Carlo Simulation is the currently accepted term for a technique used by
More informationIntegrated Cost Schedule Risk Analysis Using the Risk Driver Approach
Integrated Cost Schedule Risk Analysis Using the Risk Driver Approach Qatar PMI Meeting February 19, 2014 David T. Hulett, Ph.D. Hulett & Associates, LLC 1 The Traditional 3-point Estimate of Activity
More informationComparing Downside Risk Measures for Heavy Tailed Distributions
Comparing Downside Risk Measures for Heavy Tailed Distributions Jón Daníelsson London School of Economics Mandira Sarma Bjørn N. Jorgensen Columbia Business School Indian Statistical Institute, Delhi EURANDOM,
More informationNormal Distribution. Notes. Normal Distribution. Standard Normal. Sums of Normal Random Variables. Normal. approximation of Binomial.
Lecture 21,22, 23 Text: A Course in Probability by Weiss 8.5 STAT 225 Introduction to Probability Models March 31, 2014 Standard Sums of Whitney Huang Purdue University 21,22, 23.1 Agenda 1 2 Standard
More informationChapter 4 Continuous Random Variables and Probability Distributions
Chapter 4 Continuous Random Variables and Probability Distributions Part 2: More on Continuous Random Variables Section 4.5 Continuous Uniform Distribution Section 4.6 Normal Distribution 1 / 27 Continuous
More informationCS 237: Probability in Computing
CS 237: Probability in Computing Wayne Snyder Computer Science Department Boston University Lecture 12: Continuous Distributions Uniform Distribution Normal Distribution (motivation) Discrete vs Continuous
More informationSOCIETY OF ACTUARIES/CASUALTY ACTUARIAL SOCIETY EXAM P PROBABILITY EXAM P SAMPLE QUESTIONS
SOCIETY OF ACTUARIES/CASUALTY ACTUARIAL SOCIETY EXAM P PROBABILITY EXAM P SAMPLE QUESTIONS Copyright 007 by the Society of Actuaries and the Casualty Actuarial Society Some of the questions in this study
More informationResults for option pricing
Results for option pricing [o,v,b]=optimal(rand(1,100000 Estimators = 0.4619 0.4617 0.4618 0.4613 0.4619 o = 0.46151 % best linear combination (true value=0.46150 v = 1.1183e-005 %variance per uniform
More informationKing s College London
King s College London University Of London This paper is part of an examination of the College counting towards the award of a degree. Examinations are governed by the College Regulations under the authority
More information4.2 Probability Distributions
4.2 Probability Distributions Definition. A random variable is a variable whose value is a numerical outcome of a random phenomenon. The probability distribution of a random variable tells us what the
More informationThe Fundamentals of Reserve Variability: From Methods to Models Central States Actuarial Forum August 26-27, 2010
The Fundamentals of Reserve Variability: From Methods to Models Definitions of Terms Overview Ranges vs. Distributions Methods vs. Models Mark R. Shapland, FCAS, ASA, MAAA Types of Methods/Models Allied
More information