LECTURE 2: PROBABILITY DISTRIBUTIONS STATISTICAL ANALYSIS in EXPERIMENTAL PARTICLE PHYSICS Kai-Feng Chen National Taiwan University
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Duality for Real and Multivariate Exponential Families
Duality for real and multivariate exponential families a, G´erard Letac ∗ aInstitut de Math´ematiques de Toulouse, 118 route de Narbonne 31062 Toulouse, France. Abstract n Consider a measure µ on R generating a natural exponential family F(µ) with variance function VF(µ)(m) and Laplace transform exp(ℓµ(s)) = exp( s, x )µ(dx). ZRn −h i A dual measure µ∗ satisfies ℓ′ ( ℓ′ (s)) = s. Such a dual measure does not always exist. One important property µ∗ µ 1 − − is ℓ′′ (m) = (VF(µ)(m))− , leading to the notion of duality among exponential families (or rather among the extended µ∗ notion of T exponential families TF obtained by considering all translations of a given exponential family F). Keywords: Dilogarithm distribution, Landau distribution, large deviations, quadratic and cubic real exponential families, Tweedie scale, Wishart distributions. 2020 MSC: Primary 62H05, Secondary 60E10 1. Introduction One can be surprized by the explicit formulas that one gets from large deviations in one dimension. Consider iid random variables X1,..., Xn,... and the empirical mean Xn = (X1 + + Xn)/n. Then the Cram´er theorem [6] says that the limit ··· 1/n Pr(Xn > m) n α(m) → →∞ 1 does exist for m > E(X ). For instance, the symmetric Bernoulli case Pr(Xn = 1) = leads, for 0 < m < 1, to 1 ± 2 1 m 1+m α(m) = (1 + m)− − (1 m)− . (1) − 1 x For the bilateral exponential distribution Xn e dx we get, for m > 0, ∼ 2 −| | 1 1 √1+m2 2 α(m) = e − (1 + √1 + m ). 2 What strange formulas! Other ones can be found in [17], Problem 410. -
Luria-Delbruck Experiment
Statistical mechanics approaches for studying microbial growth (Lecture 1) Ariel Amir, 10/2019 In the following are included: 1) PDF of lecture slides 2) Calculations related to the Luria-Delbruck experiment. (See also the original paper referenced therein) 3) Calculation of the fixation probability in a serial dilution protocol, taken from SI of: Guo, Vucelja and Amir, Science Advances, 5(7), eaav3842 (2019). Statistical mechanics approaches for studying microbial growth Ariel Amir E. coli Daughter Mother cell cells Time Doubling time/ generation time/growth/replication Taheri-Araghi et al. (2015) Microbial growth 25μm Escherichia coli Saccharomyces cerevisiae Halobacterium salinarum Stewart et al., Soifer al., Eun et al., PLoS Biol (2005), Current Biology (2016) Nat. Micro (2018) Doubling time ~ tens of minutes to several hours (credit: wikipedia) Time Outline (Lecture I) • Why study microbes? Luria-Delbruck experiment, Evolution experiments • Introduction to microbial growth, with focus on cell size regulation (Lecture II) • Size control and correlations across different domains of life • Going from single-cell variability to the population growth (Lecture III) • Bet-hedging • Optimal partitioning of cellular resources Why study microbes? Luria-Delbruck experiment Protocol • Grow culture to ~108 cells. • Expose to bacteriophage. • Count survivors (plating). What to do when the experiment is not reproducible? (variance, standard deviation >> mean) Luria and Delbruck (1943) Why study microbes? Luria-Delbruck experiment Model 1: adaptation -
Choosing Between Hail Insurance and Anti-Hail Nets: a Simple Model and a Simulation Among Apples Producers in South Tyrol
Choosing Between Hail Insurance and Anti-Hail Nets: A Simple Model and a Simulation among Apples Producers in South Tyrol Marco Rogna, Günter Schamel, and Alex Weissensteiner Selected paper prepared for presentation at the 2019 Summer Symposium: Trading for Good – Agricultural Trade in the Context of Climate Change Adaptation and Mitigation: Synergies, Obstacles and Possible Solutions, co-organized by the International Agricultural Trade Research Consortium (IATRC) and the European Commission Joint Research Center (EU-JRC), June 23-25, 2019 in Seville, Spain. Copyright 2019 by Marco Rogna, Günter Schamel, and Alex Weissensteiner. All rights reserved. Readers may make verbatim copies of this document for non-commercial purposes by any means, provided that this copyright notice appears on all such copies. Choosing between Hail Insurance and Anti-Hail Nets: A Simple Model and a Simulation among Apples Producers in South Tyrol Marco Rogna∗1, G¨unter Schamely1, and Alex Weissensteinerz1 1Faculty of Economics and Management, Free University of Bozen-Bolzano, Piazza Universit´a, 1, 39100, Bozen-Bolzano, Italy. May 5, 2019 ∗[email protected] [email protected] [email protected] Abstract There is a growing interest in analysing the diffusion of agricultural insurance, seen as an effective tool for managing farm risks. Much atten- tion has been dedicated to understanding the scarce adoption rate despite high levels of subsidization and policy support. In this paper, we anal- yse an aspect that seems to have been partially overlooked: the potential competing nature between insurance and other risk management tools. We consider hail as a single source weather shock and analyse the po- tential competing effect of anti-hail nets over insurance as instruments to cope with this shock by presenting a simple theoretical model that is rooted into expected utility theory. -
Hand-Book on STATISTICAL DISTRIBUTIONS for Experimentalists
Internal Report SUF–PFY/96–01 Stockholm, 11 December 1996 1st revision, 31 October 1998 last modification 10 September 2007 Hand-book on STATISTICAL DISTRIBUTIONS for experimentalists by Christian Walck Particle Physics Group Fysikum University of Stockholm (e-mail: [email protected]) Contents 1 Introduction 1 1.1 Random Number Generation .............................. 1 2 Probability Density Functions 3 2.1 Introduction ........................................ 3 2.2 Moments ......................................... 3 2.2.1 Errors of Moments ................................ 4 2.3 Characteristic Function ................................. 4 2.4 Probability Generating Function ............................ 5 2.5 Cumulants ......................................... 6 2.6 Random Number Generation .............................. 7 2.6.1 Cumulative Technique .............................. 7 2.6.2 Accept-Reject technique ............................. 7 2.6.3 Composition Techniques ............................. 8 2.7 Multivariate Distributions ................................ 9 2.7.1 Multivariate Moments .............................. 9 2.7.2 Errors of Bivariate Moments .......................... 9 2.7.3 Joint Characteristic Function .......................... 10 2.7.4 Random Number Generation .......................... 11 3 Bernoulli Distribution 12 3.1 Introduction ........................................ 12 3.2 Relation to Other Distributions ............................. 12 4 Beta distribution 13 4.1 Introduction ....................................... -
NAG Library Chapter Introduction G01 – Simple Calculations on Statistical Data
g01 – Simple Calculations on Statistical Data Introduction – g01 NAG Library Chapter Introduction g01 – Simple Calculations on Statistical Data Contents 1 Scope of the Chapter.................................................... 2 2 Background to the Problems ............................................ 2 2.1 Summary Statistics .................................................... 2 2.2 Statistical Distribution Functions and Their Inverses........................ 2 2.3 Testing for Normality and Other Distributions ............................. 3 2.4 Distribution of Quadratic Forms......................................... 3 2.5 Energy Loss Distributions .............................................. 3 2.6 Vectorized Functions .................................................. 4 3 Recommendations on Choice and Use of Available Functions ............ 4 3.1 Working with Streamed or Extremely Large Datasets ....................... 7 4 Auxiliary Functions Associated with Library Function Arguments ....... 7 5 Functions Withdrawn or Scheduled for Withdrawal ..................... 7 6 References............................................................... 7 Mark 24 g01.1 Introduction – g01 NAG Library Manual 1 Scope of the Chapter This chapter covers three topics: summary statistics statistical distribution functions and their inverses; testing for Normality and other distributions. 2 Background to the Problems 2.1 Summary Statistics The summary statistics consist of two groups. The first group are those based on moments; for example mean, standard -
Luca Lista Statistical Methods for Data Analysis in Particle Physics Lecture Notes in Physics
Lecture Notes in Physics 909 Luca Lista Statistical Methods for Data Analysis in Particle Physics Lecture Notes in Physics Volume 909 Founding Editors W. Beiglböck J. Ehlers K. Hepp H. Weidenmüller Editorial Board M. Bartelmann, Heidelberg, Germany B.-G. Englert, Singapore, Singapore P. Hänggi, Augsburg, Germany M. Hjorth-Jensen, Oslo, Norway R.A.L. Jones, Sheffield, UK M. Lewenstein, Barcelona, Spain H. von Löhneysen, Karlsruhe, Germany J.-M. Raimond, Paris, France A. Rubio, Donostia, San Sebastian, Spain S. Theisen, Potsdam, Germany D. Vollhardt, Augsburg, Germany J.D. Wells, Ann Arbor, USA G.P. Zank, Huntsville, USA The Lecture Notes in Physics The series Lecture Notes in Physics (LNP), founded in 1969, reports new devel- opments in physics research and teaching-quickly and informally, but with a high quality and the explicit aim to summarize and communicate current knowledge in an accessible way. Books published in this series are conceived as bridging material between advanced graduate textbooks and the forefront of research and to serve three purposes: • to be a compact and modern up-to-date source of reference on a well-defined topic • to serve as an accessible introduction to the field to postgraduate students and nonspecialist researchers from related areas • to be a source of advanced teaching material for specialized seminars, courses and schools Both monographs and multi-author volumes will be considered for publication. Edited volumes should, however, consist of a very limited number of contributions only. Proceedings will not be considered for LNP. Volumes published in LNP are disseminated both in print and in electronic for- mats, the electronic archive being available at springerlink.com. -
Arxiv:1312.5000V2 [Hep-Ex] 30 Dec 2013
1 Mass distributions marginalized over per-event errors a b 2 D. Mart´ınezSantos , F. Dupertuis a 3 NIKHEF and VU University Amsterdam, Amsterdam, The Netherlands b 4 Ecole Polytechnique F´ed´erale de Lausanne (EPFL), Lausanne, Switzerland 5 Abstract 6 We present generalizations of the Crystal Ball function to describe mass peaks 7 in which the per-event mass resolution is unknown and marginalized over. The 8 presented probability density functions are tested using a series of toy MC sam- 9 ples generated with Pythia and smeared with different amounts of multiple 10 scattering and for different detector resolutions. 11 Keywords: statistics, invariant mass peaks 12 1. Introduction A very common probability density function (p:d:f:) used to fit the mass peak of a resonance in experimental particle physics is the so-called Crystal Ball (CB) function [1{3]: 8 2 − 1 ( m−µ ) m−µ <>e 2 σ , if > −a p(m) / σ (1) > m−µ n :A B − σ , otherwise 13 where m is the free variable (the measured mass), µ is the most probable value 14 (the resonance mass), σ the resolution, a is called the transition point and n the arXiv:1312.5000v2 [hep-ex] 30 Dec 2013 15 power-law exponent. A and B are calculated by imposing the continuity of the 16 function and its derivative at the transition point a. This function consists of a 17 Gaussian core, that models the detector resolution, with a tail on the left-hand 18 side that parametrizes the effect of photon radiation by the final state particles 19 in the decay. -
Field Guide to Continuous Probability Distributions
Field Guide to Continuous Probability Distributions Gavin E. Crooks v 1.0.0 2019 G. E. Crooks – Field Guide to Probability Distributions v 1.0.0 Copyright © 2010-2019 Gavin E. Crooks ISBN: 978-1-7339381-0-5 http://threeplusone.com/fieldguide Berkeley Institute for Theoretical Sciences (BITS) typeset on 2019-04-10 with XeTeX version 0.99999 fonts: Trump Mediaeval (text), Euler (math) 271828182845904 2 G. E. Crooks – Field Guide to Probability Distributions Preface: The search for GUD A common problem is that of describing the probability distribution of a single, continuous variable. A few distributions, such as the normal and exponential, were discovered in the 1800’s or earlier. But about a century ago the great statistician, Karl Pearson, realized that the known probabil- ity distributions were not sufficient to handle all of the phenomena then under investigation, and set out to create new distributions with useful properties. During the 20th century this process continued with abandon and a vast menagerie of distinct mathematical forms were discovered and invented, investigated, analyzed, rediscovered and renamed, all for the purpose of de- scribing the probability of some interesting variable. There are hundreds of named distributions and synonyms in current usage. The apparent diver- sity is unending and disorienting. Fortunately, the situation is less confused than it might at first appear. Most common, continuous, univariate, unimodal distributions can be orga- nized into a small number of distinct families, which are all special cases of a single Grand Unified Distribution. This compendium details these hun- dred or so simple distributions, their properties and their interrelations. -
Characteristic Kernels and Infinitely Divisible Distributions
Journal of Machine Learning Research 17 (2016) 1-28 Submitted 3/14; Revised 5/16; Published 9/16 Characteristic Kernels and Infinitely Divisible Distributions Yu Nishiyama [email protected] The University of Electro-Communications 1-5-1 Chofugaoka, Chofu, Tokyo 182-8585, Japan Kenji Fukumizu [email protected] The Institute of Statistical Mathematics 10-3 Midori-cho, Tachikawa, Tokyo 190-8562, Japan Editor: Ingo Steinwart Abstract We connect shift-invariant characteristic kernels to infinitely divisible distributions on Rd. Characteristic kernels play an important role in machine learning applications with their kernel means to distinguish any two probability measures. The contribution of this paper is twofold. First, we show, using the L´evy–Khintchine formula, that any shift-invariant kernel given by a bounded, continuous, and symmetric probability density function (pdf) of an infinitely divisible distribution on Rd is characteristic. We mention some closure properties of such characteristic kernels under addition, pointwise product, and convolution. Second, in developing various kernel mean algorithms, it is fundamental to compute the following values: (i) kernel mean values mP (x), x , and (ii) kernel mean RKHS inner products ∈ X mP ,mQ H, for probability measures P,Q. If P,Q, and kernel k are Gaussians, then the computationh i of (i) and (ii) results in Gaussian pdfs that are tractable. We generalize this Gaussian combination to more general cases in the class of infinitely divisible distributions. We then introduce a conjugate kernel and a convolution trick, so that the above (i) and (ii) have the same pdf form, expecting tractable computation at least in some cases. -
Crystal Ball® 7.2.2 User Manual T Corporationand Other U.S
Crystal Ball® 7.2.2 User Manual This manual, and the software described in it, are furnished under license and may only be used or copied in accordance with the terms of the license agreement. Information in this document is provided for informational purposes only, is subject to change without notice, and does not represent a commitment as to merchantability or fitness for a particular purpose by Decisioneering, Inc. No part of this manual may be reproduced or transmitted in any form or by any means, electronic or mechanical, including photocopying and recording, for any purpose without the express written permission of Decisioneering, Inc. Written, designed, and published in the United States of America. To purchase additional copies of this document, contact the Technical Services or Sales Department at the address below: Decisioneering, Inc. 1515 Arapahoe St., Suite 1311 Denver, Colorado, USA 80202 Phone: +1 303-534-1515 Toll-free sales: 1-800-289-2550 Fax: 1-303-534-4818 © 1988-2006, Decisioneering, Inc. Decisioneering® is a registered trademark of Decisioneering, Inc. Crystal Ball® is a registered trademark of Decisioneering, Inc. CB Predictor™ is a trademark of Decisioneering, Inc. OptQuest® is a registered trademark of Optimization Technologies, Inc. Microsoft® is a registered trademark of Microsoft Corporation in the U.S. and other countries. FLEXlm™ is a trademark of Macrovision Corporation. Chart FX® is a registered trademark of Software FX, Inc. is a registered trademark of Frontline Systems, Inc. Other product names mentioned herein may be trademarks and/or registered trademarks of the respective holders. MAN-CBUM 070202-2 6/15/06 Contents Welcome to Crystal Ball® Who should use this program ...................................................................1 What you will need ....................................................................................1 About the Crystal Ball documentation set ................................................2 Conventions used in this manual ................................................................... -
Roofit Users Manual V2.91 W
Document version 2.91-33 – October 14, 2008 RooFit Users Manual v2.91 W. Verkerke, D. Kirkby Table of Contents Table of Contents .................................................................................................................................... 2 What is RooFit? ....................................................................................................................................... 4 1. Installation and setup of RooFit .......................................................................................................... 6 2. Getting started ..................................................................................................................................... 7 Building a model .................................................................................................................................. 7 Visualizing a model .............................................................................................................................. 7 Importing data ...................................................................................................................................... 9 Fitting a model to data ....................................................................................................................... 10 Generating data from a model ........................................................................................................... 13 Parameters and observables ........................................................................................................... -
Statistical Data Analysis, Clarendon Press, Oxford, 1998 G
E. Santovetti Università degli Studi di Roma Tor Vergata StatisticalStatistical datadata analysisanalysis lecturelecture II Useful books: G. Cowan, Statistical Data Analysis, Clarendon Press, Oxford, 1998 G. D'Agostini: "Bayesian reasoning in data analysis - A critical introduction", World Scientific Publishing 2003 1 DataData analysisanalysis inin particleparticle physicsphysics Aim of experimental particle physics is to find or build environments, able to test the theoretical models, e.g. Standard Model (SM). In particle physics we study the result of an interaction and measure several quantities for each produced particles (charge, momentum, energy …) e+ e- Tasks of the data analysis is: Measure (estimate) the parameters; Quantify the uncertainty of the parameter estimates; Test the extent to which the predictions of a theory are in agreement with the data. There are several sources of uncertainty: Theory is not deterministic (quantum mechanics) Random measurement fluctuations, even without quantum effects Errors due to nonfunctional instruments or procedures We can quantify the uncertainty using probability 2 DefinitionDefinition In probability theory, the probability P of some event A, denoted with P(A), is usually defined in such a way that P satisfies the Kolmogorov axioms: 1) The probability of an event is a non negative real number P ( A)≥0 ∀A∈S 2) The total (maximal) probability is one P(S )=1 3) If two events are pairwise disjoint, the probability of the two events is the sum of the two probabilities A∩B=∅ ⇒ P ( A∪B)=P ( A)+ P (B) From this axioms we can derive further properties: P( A)=1−P( A) P( A∪A)=1 P(∅)=0 A⊂B ⇒ P( A)≤P(B) P( A∪B)=P( A)+P(B)−P( A∩B) 3 Andrey Kolmogorov, 1933 ConditionalConditional probability,probability, independenceindependence An important concept to introduce is the conditional probability: probability of A, given B (with P(B)≠0).