where the optical depth is Z
|
|
- Zoe Stafford
- 6 years ago
- Views:
Transcription
1 Curve of Growth The curve of growth describes how the line strength increases as the optical depth increases. An appropriate measure of line strength is the equivalent width: the area of a rectangle with the same area as the line profile and height equal to the continuum. Webegin with apurelyabsorbing, simple slab modelwith radiation incident on one side. Then from the solution to the transfer equation, we have the intensity emerging: I ν = I e τn where the optical depth is τ n = n x κ φ v dl ' n x Lκ φ v where n x is the density of atoms of element x, L is the thickness of the slab, κ is the absorption coecent per atom at line center, and φ ν is the line profile function. The factor R n x dl N x ' n x L is called the column density The area under the curve I ν (ν) is R I ν dν and the area of the line is R (I I ν ) dν The equivalent width of the line is defined to be µ W = 1 I ν dν = 1 e N xκ φ v dν I Of course this is an idealization. In practice the limits are frequencies far enough from the line center that we are clearly in the continuum. In the classical oscillator model, ³ hν/kt κ = πe2 mc f 1 e where the oscillator strength f takes quantum e ects into account and ν is the frequency at line center. The line profile function depends on the broadening mechanisms in e ect. 1. We always have Doppler broadening due to the velocity distribution of the atoms. In LTE, " µ # 2 1 ν ν φ ν,doppler = p exp πνd ν D 1
2 and the line breadth factor is ν D = ν c 2kT M + (turbulent velocity)2 1/2 2. Natural broadening is due to radiation reaction (my notes, R&L pg 287, Shore pg 197-8) γ φ ν = 4π 2 (ν ν ) 2 + (γ/2) 2 This is sometimes called the Lorentz profile. We can include the e ect of collisions by adding a term γ C to the natural broadening damping factor γ..1 Weak lines N x κ φ 1 Then W = 1 1 e N xκ φ v dν ' N x κ φ v dν = N x κ φ v dν = N x κ The result is independent of the shape of φ ν and thus of the type of broadening. The equivalent width is linearly proportional to the column density..2 Strong lines τ = N x κ φ À 1 Notice that N x κ φ ν cannot be À 1 everywhere, since φ ν! far from the line center. Let the frequency where τ ν ' 1 be ν. Then W ' ν ν e N xκ φ v dν+ 1 e N xκ φ v dν+ ν ν + In the middle factor, e τν is 1. The two other terms are each of order N x κ ν + φ v dν N x κ 1 e N xκ φ v dν and we neglect them. Thus W ' 2 2
3 .2.1 Moderately strong lines In these lines N x κ φ ν becomes <1 while still in the Doppler core. So N x κ φ v (ν + ) = 1 " µ 2 # 1 N x κ p exp = 1 πνd ν D µ Nx κ = ν D sln p πνd where the square root is. 1 for self consistency, and it follows that µ Nx κ W ' 2ν D sln p πνd Thus the equivalent width increases logarithmically with N. Check that the terms we neglected are small: W neglected W kept So the approximations are consistent..2.2 Very strong lines N x κ ³ 2ν D rln pπνd. e Nx κ 2» 1 Here N x κ φ ν does not become <1 until the wings of the line. Thus the transition occurs at where N x κ γ 4π (γ/2) 2 = 1 Uusally γ 2, so we may approximate: p Nx κ γ = 2π and then p Nx κ γ W ' π and increases as the square root of N. Check as in the previous case. The neglected term is: N x κ γ 4π 2 x 2 + (γ/2) 2 dx = N xκ γ 4π 2 = N xκ γ 4π 4π 2 γ» N xκ γ 4π 2» 3 dx x 2 + γ 2 /16π 2 µ π 2 4π tan 1 γ p Nx κ γ 2π
4 Thus we get the curve of growth: let x = N xκ ν D and y = W/ν D then The transition to the saturated regime occurs sooner (lower W) for lower temperatures because ν D is smaller. The damped (square root) portion is higher is the damping paramater γ is larger. The line profile becomes squarer in the saturated portion abd devlops broad wings in the damping portion. (See figure on next page.) Touse the curve of growth one compares the theoretical curve with an emipiricalcurve contructed from the data. The column density and temperature can then be deduced. One can use line multiplets of the same atom, or di erent trancitions (but beware here if γ is dominated by naturalrather than collisional broadening.) Here s some data for hydrogen from b is the Doppler parameter ν D in velocity units: b = p 2kT/m and m is the atomic mass. 4
5 Here s another one from 5
6 6
7 Q z=3.12 Lya Forest + metals Ly-limit Damped Lya QSO Lya em. 7
Statistical Tables Compiled by Alan J. Terry
Statistical Tables Compiled by Alan J. Terry School of Science and Sport University of the West of Scotland Paisley, Scotland Contents Table 1: Cumulative binomial probabilities Page 1 Table 2: Cumulative
More informationProblems from 9th edition of Probability and Statistical Inference by Hogg, Tanis and Zimmerman:
Math 224 Fall 207 Homework 5 Drew Armstrong Problems from 9th edition of Probability and Statistical Inference by Hogg, Tanis and Zimmerman: Section 3., Exercises 3, 0. Section 3.3, Exercises 2, 3, 0,.
More informationSTAT:2010 Statistical Methods and Computing. Using density curves to describe the distribution of values of a quantitative
STAT:10 Statistical Methods and Computing Normal Distributions Lecture 4 Feb. 6, 17 Kate Cowles 374 SH, 335-0727 kate-cowles@uiowa.edu 1 2 Using density curves to describe the distribution of values of
More informationSimulation Wrap-up, Statistics COS 323
Simulation Wrap-up, Statistics COS 323 Today Simulation Re-cap Statistics Variance and confidence intervals for simulations Simulation wrap-up FYI: No class or office hours Thursday Simulation wrap-up
More informationConfidence Intervals. σ unknown, small samples The t-statistic /22
Confidence Intervals σ unknown, small samples The t-statistic 1 /22 Homework Read Sec 7-3. Discussion Question pg 365 Do Ex 7-3 1-4, 6, 9, 12, 14, 15, 17 2/22 Objective find the confidence interval for
More informationSome statistical properties of surface slopes via remote sensing considering a non-gaussian probability density function
Journal of odern Optics ISSN: 0950-0340 Print 36-3044 Online Journal homepage: http://www.tandfonline.com/loi/tmop0 Some statistical properties of surface slopes via remote sensing considering a non-gaussian
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 informationPosterior Inference. , where should we start? Consider the following computational procedure: 1. draw samples. 2. convert. 3. compute properties
Posterior Inference Example. Consider a binomial model where we have a posterior distribution for the probability term, θ. Suppose we want to make inferences about the log-odds γ = log ( θ 1 θ), where
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 informationNEWCASTLE UNIVERSITY SCHOOL OF MATHEMATICS, STATISTICS & PHYSICS SEMESTER 1 SPECIMEN 2 MAS3904. Stochastic Financial Modelling. Time allowed: 2 hours
NEWCASTLE UNIVERSITY SCHOOL OF MATHEMATICS, STATISTICS & PHYSICS SEMESTER 1 SPECIMEN 2 Stochastic Financial Modelling Time allowed: 2 hours Candidates should attempt all questions. Marks for each question
More informationA Long-Run, Short-Run and Politico-Economic Analysis of the Welfare Costs of In ation
A Long-Run, Short-Run and Politico-Economic Analysis of the Welfare Costs of In ation Scott J. Dressler Villanova University Summer Workshop on Money, Banking, Payments and Finance August 17, 2011 Motivation
More informationA continuous random variable is one that can theoretically take on any value on some line interval. We use f ( x)
Section 6-2 I. Continuous Probability Distributions A continuous random variable is one that can theoretically take on any value on some line interval. We use f ( x) to represent a probability density
More informationAsymmetric fan chart a graphical representation of the inflation prediction risk
Asymmetric fan chart a graphical representation of the inflation prediction ASYMMETRIC DISTRIBUTION OF THE PREDICTION RISK The uncertainty of a prediction is related to the in the input assumptions for
More informationStatistics for Business and Economics
Statistics for Business and Economics Chapter 5 Continuous Random Variables and Probability Distributions Ch. 5-1 Probability Distributions Probability Distributions Ch. 4 Discrete Continuous Ch. 5 Probability
More informationCentral Limit Theorem, Joint Distributions Spring 2018
Central Limit Theorem, Joint Distributions 18.5 Spring 218.5.4.3.2.1-4 -3-2 -1 1 2 3 4 Exam next Wednesday Exam 1 on Wednesday March 7, regular room and time. Designed for 1 hour. You will have the full
More informationChapter ! Bell Shaped
Chapter 6 6-1 Business Statistics: A First Course 5 th Edition Chapter 7 Continuous Probability Distributions Learning Objectives In this chapter, you learn:! To compute probabilities from the normal distribution!
More informationChapter 6 Analyzing Accumulated Change: Integrals in Action
Chapter 6 Analyzing Accumulated Change: Integrals in Action 6. Streams in Business and Biology You will find Excel very helpful when dealing with streams that are accumulated over finite intervals. Finding
More informationContinuous random variables
Continuous random variables probability density function (f(x)) the probability distribution function of a continuous random variable (analogous to the probability mass function for a discrete random variable),
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 / 28 One more
More informationStatistical Intervals. Chapter 7 Stat 4570/5570 Material from Devore s book (Ed 8), and Cengage
7 Statistical Intervals Chapter 7 Stat 4570/5570 Material from Devore s book (Ed 8), and Cengage Confidence Intervals The CLT tells us that as the sample size n increases, the sample mean X is close to
More informationChapter 3: Black-Scholes Equation and Its Numerical Evaluation
Chapter 3: Black-Scholes Equation and Its Numerical Evaluation 3.1 Itô Integral 3.1.1 Convergence in the Mean and Stieltjes Integral Definition 3.1 (Convergence in the Mean) A sequence {X n } n ln of random
More informationLecture 12. Some Useful Continuous Distributions. The most important continuous probability distribution in entire field of statistics.
ENM 207 Lecture 12 Some Useful Continuous Distributions Normal Distribution The most important continuous probability distribution in entire field of statistics. Its graph, called the normal curve, is
More informationStatistical Intervals (One sample) (Chs )
7 Statistical Intervals (One sample) (Chs 8.1-8.3) Confidence Intervals The CLT tells us that as the sample size n increases, the sample mean X is close to normally distributed with expected value µ and
More informationModule Tag PSY_P2_M 7. PAPER No.2: QUANTITATIVE METHODS MODULE No.7: NORMAL DISTRIBUTION
Subject Paper No and Title Module No and Title Paper No.2: QUANTITATIVE METHODS Module No.7: NORMAL DISTRIBUTION Module Tag PSY_P2_M 7 TABLE OF CONTENTS 1. Learning Outcomes 2. Introduction 3. Properties
More informationPractice Exam 1. Loss Amount Number of Losses
Practice Exam 1 1. You are given the following data on loss sizes: An ogive is used as a model for loss sizes. Determine the fitted median. Loss Amount Number of Losses 0 1000 5 1000 5000 4 5000 10000
More informationConvergence of statistical moments of particle density time series in scrape-off layer plasmas
Convergence of statistical moments of particle density time series in scrape-off layer plasmas R. Kube and O. E. Garcia Particle density fluctuations in the scrape-off layer of magnetically confined plasmas,
More informationSTATE UNIVERSITY OF NEW YORK AT ALBANY Department of Economics. Ph. D. Comprehensive Examination: Macroeconomics Fall, 2010
STATE UNIVERSITY OF NEW YORK AT ALBANY Department of Economics Ph. D. Comprehensive Examination: Macroeconomics Fall, 2010 Section 1. (Suggested Time: 45 Minutes) For 3 of the following 6 statements, state
More information"Pricing Exotic Options using Strong Convergence Properties
Fourth Oxford / Princeton Workshop on Financial Mathematics "Pricing Exotic Options using Strong Convergence Properties Klaus E. Schmitz Abe schmitz@maths.ox.ac.uk www.maths.ox.ac.uk/~schmitz Prof. Mike
More information5.7 Probability Distributions and Variance
160 CHAPTER 5. PROBABILITY 5.7 Probability Distributions and Variance 5.7.1 Distributions of random variables We have given meaning to the phrase expected value. For example, if we flip a coin 100 times,
More informationFinal Examination Re - Calculus I 21 December 2015
. (5 points) Given the graph of f below, determine each of the following. Use, or does not exist where appropriate. y (a) (b) x 3 x 2 + (c) x 2 (d) x 2 (e) f(2) = (f) x (g) x (h) f (3) = 3 2 6 5 4 3 2
More informationThe stochastic calculus
Gdansk A schedule of the lecture Stochastic differential equations Ito calculus, Ito process Ornstein - Uhlenbeck (OU) process Heston model Stopping time for OU process Stochastic differential equations
More informationIntroduction to Business Statistics QM 120 Chapter 6
DEPARTMENT OF QUANTITATIVE METHODS & INFORMATION SYSTEMS Introduction to Business Statistics QM 120 Chapter 6 Spring 2008 Chapter 6: Continuous Probability Distribution 2 When a RV x is discrete, we can
More informationLecture 3: Review of Probability, MATLAB, Histograms
CS 4980/6980: Introduction to Data Science c Spring 2018 Lecture 3: Review of Probability, MATLAB, Histograms Instructor: Daniel L. Pimentel-Alarcón Scribed and Ken Varghese This is preliminary work and
More informationContinuous Probability Distributions & Normal Distribution
Mathematical Methods Units 3/4 Student Learning Plan Continuous Probability Distributions & Normal Distribution 7 lessons Notes: Students need practice in recognising whether a problem involves a discrete
More informationROM SIMULATION Exact Moment Simulation using Random Orthogonal Matrices
ROM SIMULATION Exact Moment Simulation using Random Orthogonal Matrices Bachelier Finance Society Meeting Toronto 2010 Henley Business School at Reading Contact Author : d.ledermann@icmacentre.ac.uk Alexander
More informationPart 1: q Theory and Irreversible Investment
Part 1: q Theory and Irreversible Investment Goal: Endogenize firm characteristics and risk. Value/growth Size Leverage New issues,... This lecture: q theory of investment Irreversible investment and real
More informationModeling dynamic diurnal patterns in high frequency financial data
Modeling dynamic diurnal patterns in high frequency financial data Ryoko Ito 1 Faculty of Economics, Cambridge University Email: ri239@cam.ac.uk Website: www.itoryoko.com This paper: Cambridge Working
More informationHousehold Debt, Financial Intermediation, and Monetary Policy
Household Debt, Financial Intermediation, and Monetary Policy Shutao Cao 1 Yahong Zhang 2 1 Bank of Canada 2 Western University October 21, 2014 Motivation The US experience suggests that the collapse
More informationInterest rate models and Solvency II
www.nr.no Outline Desired properties of interest rate models in a Solvency II setting. A review of three well-known interest rate models A real example from a Norwegian insurance company 2 Interest rate
More informationدرس هفتم یادگیري ماشین. (Machine Learning) دانشگاه فردوسی مشهد دانشکده مهندسی رضا منصفی
یادگیري ماشین توزیع هاي نمونه و تخمین نقطه اي پارامترها Sampling Distributions and Point Estimation of Parameter (Machine Learning) دانشگاه فردوسی مشهد دانشکده مهندسی رضا منصفی درس هفتم 1 Outline Introduction
More informationSupplemental Material Optics formula and additional results
Supplemental Material Optics formula and additional results Fresnel equations We reproduce the Fresnel equations derived from Maxwell equations as given by Born and Wolf (Section 4.4.). They correspond
More informationSaddlepoint Approximation Methods for Pricing. Financial Options on Discrete Realized Variance
Saddlepoint Approximation Methods for Pricing Financial Options on Discrete Realized Variance Yue Kuen KWOK Department of Mathematics Hong Kong University of Science and Technology Hong Kong * This is
More information4: Probability. What is probability? Random variables (RVs)
4: Probability b binomial µ expected value [parameter] n number of trials [parameter] N normal p probability of success [parameter] pdf probability density function pmf probability mass function RV random
More informationLikelihood Methods of Inference. Toss coin 6 times and get Heads twice.
Methods of Inference Toss coin 6 times and get Heads twice. p is probability of getting H. Probability of getting exactly 2 heads is 15p 2 (1 p) 4 This function of p, is likelihood function. Definition:
More informationSTART HERE: Instructions. 1 Exponential Family [Zhou, Manzil]
START HERE: Instructions Thanks a lot to John A.W.B. Constanzo and Shi Zong for providing and allowing to use the latex source files for quick preparation of the HW solution. The homework was due at 9:00am
More informationProbability theory: basic notions
1 Probability theory: basic notions All epistemologic value of the theory of probability is based on this: that large scale random phenomena in their collective action create strict, non random regularity.
More informationProbability Distributions II
Probability Distributions II Summer 2017 Summer Institutes 63 Multinomial Distribution - Motivation Suppose we modified assumption (1) of the binomial distribution to allow for more than two outcomes.
More informationExpected Value of a Random Variable
Knowledge Article: Probability and Statistics Expected Value of a Random Variable Expected Value of a Discrete Random Variable You're familiar with a simple mean, or average, of a set. The mean value of
More informationModeling the extremes of temperature time series. Debbie J. Dupuis Department of Decision Sciences HEC Montréal
Modeling the extremes of temperature time series Debbie J. Dupuis Department of Decision Sciences HEC Montréal Outline Fig. 1: S&P 500. Daily negative returns (losses), Realized Variance (RV) and Jump
More informationTheoretical Foundations
Theoretical Foundations Probabilities Monia Ranalli monia.ranalli@uniroma2.it Ranalli M. Theoretical Foundations - Probabilities 1 / 27 Objectives understand the probability basics quantify random phenomena
More informationContinuous Probability Distributions
8.1 Continuous Probability Distributions Distributions like the binomial probability distribution and the hypergeometric distribution deal with discrete data. The possible values of the random variable
More informationECSE B Assignment 5 Solutions Fall (a) Using whichever of the Markov or the Chebyshev inequalities is applicable, estimate
ECSE 304-305B Assignment 5 Solutions Fall 2008 Question 5.1 A positive scalar random variable X with a density is such that EX = µ
More informationChapter 2 Rocket Launch: AREA BETWEEN CURVES
ANSWERS Mathematics (Mathematical Analysis) page 1 Chapter Rocket Launch: AREA BETWEEN CURVES RL-. a) 1,.,.; $8, $1, $18, $0, $, $6, $ b) x; 6(x ) + 0 RL-. a), 16, 9,, 1, 0; 1,,, 7, 9, 11 c) D = (-, );
More information. Fiscal Reform and Government Debt in Japan: A Neoclassical Perspective. May 10, 2013
.. Fiscal Reform and Government Debt in Japan: A Neoclassical Perspective Gary Hansen (UCLA) and Selo İmrohoroğlu (USC) May 10, 2013 Table of Contents.1 Introduction.2 Model Economy.3 Calibration.4 Quantitative
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 informationCoale & Kisker approach
Coale & Kisker approach Often actuaries need to extrapolate mortality at old ages. Many authors impose q120 =1but the latter constraint is not compatible with forces of mortality; here, we impose µ110
More informationResearch at Intersection of Trade and IO. Interest in heterogeneous impact of trade policy (some firms win, others lose, perhaps in same industry)
Research at Intersection of Trade and IO Countries don t export, plant s export Interest in heterogeneous impact of trade policy (some firms win, others lose, perhaps in same industry) (Whatcountriesa
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 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 informationProbability distributions relevant to radiowave propagation modelling
Rec. ITU-R P.57 RECOMMENDATION ITU-R P.57 PROBABILITY DISTRIBUTIONS RELEVANT TO RADIOWAVE PROPAGATION MODELLING (994) Rec. ITU-R P.57 The ITU Radiocommunication Assembly, considering a) that the propagation
More informationObjective Bayesian Analysis for Heteroscedastic Regression
Analysis for Heteroscedastic Regression & Esther Salazar Universidade Federal do Rio de Janeiro Colóquio Inter-institucional: Modelos Estocásticos e Aplicações 2009 Collaborators: Marco Ferreira and Thais
More informationSlides for Risk Management Credit Risk
Slides for Risk Management Credit Risk Groll Seminar für Finanzökonometrie Prof. Mittnik, PhD Groll (Seminar für Finanzökonometrie) Slides for Risk Management Prof. Mittnik, PhD 1 / 97 1 Introduction to
More informationDepartment of Quantitative Methods & Information Systems. Business Statistics. Chapter 6 Normal Probability Distribution QMIS 120. Dr.
Department of Quantitative Methods & Information Systems Business Statistics Chapter 6 Normal Probability Distribution QMIS 120 Dr. Mohammad Zainal Chapter Goals After completing this chapter, you should
More informationCategorical. A general name for non-numerical data; the data is separated into categories of some kind.
Chapter 5 Categorical A general name for non-numerical data; the data is separated into categories of some kind. Nominal data Categorical data with no implied order. Eg. Eye colours, favourite TV show,
More informationIntroduction to Sequential Monte Carlo Methods
Introduction to Sequential Monte Carlo Methods Arnaud Doucet NCSU, October 2008 Arnaud Doucet () Introduction to SMC NCSU, October 2008 1 / 36 Preliminary Remarks Sequential Monte Carlo (SMC) are a set
More informationChapter 4: Commonly Used Distributions. Statistics for Engineers and Scientists Fourth Edition William Navidi
Chapter 4: Commonly Used Distributions Statistics for Engineers and Scientists Fourth Edition William Navidi 2014 by Education. This is proprietary material solely for authorized instructor use. Not authorized
More informationSTAT Chapter 5: Continuous Distributions. Probability distributions are used a bit differently for continuous r.v. s than for discrete r.v. s.
STAT 515 -- Chapter 5: Continuous Distributions Probability distributions are used a bit differently for continuous r.v. s than for discrete r.v. s. Continuous distributions typically are represented by
More informationFinite Element Method
In Finite Difference Methods: the solution domain is divided into a grid of discrete points or nodes the PDE is then written for each node and its derivatives replaced by finite-divided differences In
More informationLecture 5: Labour Economics and Wage-Setting Theory
Lecture 5: Labour Economics and Wage-Setting Theory Spring 2014 Lars Calmfors Literature: Chapter 7 Cahuc-Zylberberg (pp 393-403) 1 Topics Weakly efficient bargaining Strongly efficient bargaining Wage
More informationMS-E2114 Investment Science Exercise 10/2016, Solutions
A simple and versatile model of asset dynamics is the binomial lattice. In this model, the asset price is multiplied by either factor u (up) or d (down) in each period, according to probabilities p and
More informationToward A Term Structure of Macroeconomic Risk
Toward A Term Structure of Macroeconomic Risk Pricing Unexpected Growth Fluctuations Lars Peter Hansen 1 2007 Nemmers Lecture, Northwestern University 1 Based in part joint work with John Heaton, Nan Li,
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 informationQuestion 1 Consider an economy populated by a continuum of measure one of consumers whose preferences are defined by the utility function:
Question 1 Consider an economy populated by a continuum of measure one of consumers whose preferences are defined by the utility function: β t log(c t ), where C t is consumption and the parameter β satisfies
More informationSAQ KONTROLL AB Box 49306, STOCKHOLM, Sweden Tel: ; Fax:
ProSINTAP - A Probabilistic Program for Safety Evaluation Peter Dillström SAQ / SINTAP / 09 SAQ KONTROLL AB Box 49306, 100 29 STOCKHOLM, Sweden Tel: +46 8 617 40 00; Fax: +46 8 651 70 43 June 1999 Page
More informationRECURSIVE VALUATION AND SENTIMENTS
1 / 32 RECURSIVE VALUATION AND SENTIMENTS Lars Peter Hansen Bendheim Lectures, Princeton University 2 / 32 RECURSIVE VALUATION AND SENTIMENTS ABSTRACT Expectations and uncertainty about growth rates that
More informationSquare-Root Measurement for Ternary Coherent State Signal
ISSN 86-657 Square-Root Measurement for Ternary Coherent State Signal Kentaro Kato Quantum ICT Research Institute, Tamagawa University 6-- Tamagawa-gakuen, Machida, Tokyo 9-86, Japan Tamagawa University
More informationExtended Libor Models and Their Calibration
Extended Libor Models and Their Calibration Denis Belomestny Weierstraß Institute Berlin Vienna, 16 November 2007 Denis Belomestny (WIAS) Extended Libor Models and Their Calibration Vienna, 16 November
More information2011 Pearson Education, Inc
Statistics for Business and Economics Chapter 4 Random Variables & Probability Distributions Content 1. Two Types of Random Variables 2. Probability Distributions for Discrete Random Variables 3. The Binomial
More informationWe follow Agarwal, Driscoll, and Laibson (2012; henceforth, ADL) to estimate the optimal, (X2)
Online appendix: Optimal refinancing rate We follow Agarwal, Driscoll, and Laibson (2012; henceforth, ADL) to estimate the optimal refinance rate or, equivalently, the optimal refi rate differential. In
More informationPrice manipulation in models of the order book
Price manipulation in models of the order book Jim Gatheral (including joint work with Alex Schied) RIO 29, Búzios, Brasil Disclaimer The opinions expressed in this presentation are those of the author
More informationChapter 8 Estimation
Chapter 8 Estimation There are two important forms of statistical inference: estimation (Confidence Intervals) Hypothesis Testing Statistical Inference drawing conclusions about populations based on samples
More informationConjugate Models. Patrick Lam
Conjugate Models Patrick Lam Outline Conjugate Models What is Conjugacy? The Beta-Binomial Model The Normal Model Normal Model with Unknown Mean, Known Variance Normal Model with Known Mean, Unknown Variance
More informationTFP Persistence and Monetary Policy. NBS, April 27, / 44
TFP Persistence and Monetary Policy Roberto Pancrazi Toulouse School of Economics Marija Vukotić Banque de France NBS, April 27, 2012 NBS, April 27, 2012 1 / 44 Motivation 1 Well Known Facts about the
More informationLecture 2. Probability Distributions Theophanis Tsandilas
Lecture 2 Probability Distributions Theophanis Tsandilas Comment on measures of dispersion Why do common measures of dispersion (variance and standard deviation) use sums of squares: nx (x i ˆµ) 2 i=1
More information2 Exploring Univariate Data
2 Exploring Univariate Data A good picture is worth more than a thousand words! Having the data collected we examine them to get a feel for they main messages and any surprising features, before attempting
More informationSUPPLEMENTARY INFORMATION
Realizing effective magnetic field for photons by controlling the phase of dynamic modulation: Supplementary information Kejie Fang Department of Physics, Stanford University, Stanford, California 94305,
More informationFinancial Risk Management
Financial Risk Management Professor: Thierry Roncalli Evry University Assistant: Enareta Kurtbegu Evry University Tutorial exercices #3 1 Maximum likelihood of the exponential distribution 1. We assume
More informationQuantitative Methods for Economics, Finance and Management (A86050 F86050)
Quantitative Methods for Economics, Finance and Management (A86050 F86050) Matteo Manera matteo.manera@unimib.it Marzio Galeotti marzio.galeotti@unimi.it 1 This material is taken and adapted from Guy Judge
More informationBayesian Linear Model: Gory Details
Bayesian Linear Model: Gory Details Pubh7440 Notes By Sudipto Banerjee Let y y i ] n i be an n vector of independent observations on a dependent variable (or response) from n experimental units. Associated
More informationMeasures of Dispersion (Range, standard deviation, standard error) Introduction
Measures of Dispersion (Range, standard deviation, standard error) Introduction We have already learnt that frequency distribution table gives a rough idea of the distribution of the variables in a sample
More informationSLE and CFT. Mitsuhiro QFT2005
SLE and CFT Mitsuhiro Kato @ QFT2005 1. Introduction Critical phenomena Conformal Field Theory (CFT) Algebraic approach, field theory, BPZ(1984) Stochastic Loewner Evolution (SLE) Geometrical approach,
More informationDynamic Response of Jackup Units Re-evaluation of SNAME 5-5A Four Methods
ISOPE 2010 Conference Beijing, China 24 June 2010 Dynamic Response of Jackup Units Re-evaluation of SNAME 5-5A Four Methods Xi Ying Zhang, Zhi Ping Cheng, Jer-Fang Wu and Chee Chow Kei ABS 1 Main Contents
More informationSDP Macroeconomics Final exam, 2014 Professor Ricardo Reis
SDP Macroeconomics Final exam, 2014 Professor Ricardo Reis Answer each question in three or four sentences and perhaps one equation or graph. Remember that the explanation determines the grade. 1. Question
More informationModes of Exports by Sub-Saharan African Firms: Intensive Margins and Interdependencies
Modes of Exports by Sub-Saharan African Firms: Intensive Margins and Interdependencies Seifu Zerihun and Sajal Lahiri Caterpillar Inc. and Southern Illinois University ( seifezerihun@yahoo.com and lahiri@siu.edu)
More informationPreference Shocks, Liquidity Shocks, and Price Dynamics
Preference Shocks, Liquidity Shocks, and Price Dynamics Nao Sudo 21st April 21 at GRIPS () 21st April 21 at GRIPS 1 / 47 Directions Motivation Literature Model Extracting Shocks (BOJ) 21st April 21 at
More informationThe Normal Distribution
Will Monroe CS 09 The Normal Distribution Lecture Notes # July 9, 207 Based on a chapter by Chris Piech The single most important random variable type is the normal a.k.a. Gaussian) random variable, parametrized
More informationOption Pricing for Discrete Hedging and Non-Gaussian Processes
Option Pricing for Discrete Hedging and Non-Gaussian Processes Kellogg College University of Oxford A thesis submitted in partial fulfillment of the requirements for the MSc in Mathematical Finance November
More informationReal Business Cycles in Emerging Countries?
Real Business Cycles in Emerging Countries? Javier García-Cicco, Roberto Pancrazi and Martín Uribe Published in American Economic Review (2010) Presented by Onursal Bağırgan Real Business Cycles in Emerging
More informationData that can be any numerical value are called continuous. These are usually things that are measured, such as height, length, time, speed, etc.
Chapter 8 Measures of Center Data that can be any numerical value are called continuous. These are usually things that are measured, such as height, length, time, speed, etc. Data that can only be integer
More informationCSC Advanced Scientific Programming, Spring Descriptive Statistics
CSC 223 - Advanced Scientific Programming, Spring 2018 Descriptive Statistics Overview Statistics is the science of collecting, organizing, analyzing, and interpreting data in order to make decisions.
More information