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Arxiv:1906.06684V1 [Math.NA]
Randomized Computation of Continuous Data: Is Brownian Motion Computable?∗ Willem L. Fouch´e1, Hyunwoo Lee2, Donghyun Lim2, Sewon Park2, Matthias Schr¨oder3, Martin Ziegler2 1 University of South Africa 2 KAIST 3 University of Birmingham Abstract. We consider randomized computation of continuous data in the sense of Computable Analysis. Our first contribution formally confirms that it is no loss of generality to take as sample space the Cantor space of infinite fair coin flips. This extends [Schr¨oder&Simpson’05] and [Hoyrup&Rojas’09] considering sequences of suitably and adaptively biased coins. Our second contribution is concerned with 1D Brownian Motion (aka Wiener Process), a prob- ability distribution on the space of continuous functions f : [0, 1] → R with f(0) = 0 whose computability has been conjectured [Davie&Fouch´e’2013; arXiv:1409.4667,§6]. We establish that this (higher-type) random variable is computable iff some/every computable family of moduli of continuity (as ordinary random variables) has a computable probability distribution with respect to the Wiener Measure. 1 Introduction Randomization is a powerful technique in classical (i.e. discrete) Computer Science: Many difficult problems have turned out to admit simple solutions by algorithms that ‘roll dice’ and are efficient/correct/optimal with high probability [DKM+94,BMadHS99,CS00,BV04]. Indeed, fair coin flips have been shown computationally universal [Wal77]. Over continuous data, well-known closely connected to topology [Grz57] [Wei00, 2.2+ 3], notions of proba- § § bilistic computation are more subtle [BGH15,Col15]. 1.1 Overview Section 2 resumes from [SS06] the question of how to represent Borel probability measures. -
A Unit-Error Theory for Register-Based Household Statistics
A Service of Leibniz-Informationszentrum econstor Wirtschaft Leibniz Information Centre Make Your Publications Visible. zbw for Economics Zhang, Li-Chun Working Paper A unit-error theory for register-based household statistics Discussion Papers, No. 598 Provided in Cooperation with: Research Department, Statistics Norway, Oslo Suggested Citation: Zhang, Li-Chun (2009) : A unit-error theory for register-based household statistics, Discussion Papers, No. 598, Statistics Norway, Research Department, Oslo This Version is available at: http://hdl.handle.net/10419/192580 Standard-Nutzungsbedingungen: Terms of use: Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Documents in EconStor may be saved and copied for your Zwecken und zum Privatgebrauch gespeichert und kopiert werden. personal and scholarly purposes. Sie dürfen die Dokumente nicht für öffentliche oder kommerzielle You are not to copy documents for public or commercial Zwecke vervielfältigen, öffentlich ausstellen, öffentlich zugänglich purposes, to exhibit the documents publicly, to make them machen, vertreiben oder anderweitig nutzen. publicly available on the internet, or to distribute or otherwise use the documents in public. Sofern die Verfasser die Dokumente unter Open-Content-Lizenzen (insbesondere CC-Lizenzen) zur Verfügung gestellt haben sollten, If the documents have been made available under an Open gelten abweichend von diesen Nutzungsbedingungen die in der dort Content Licence (especially Creative Commons Licences), you genannten Lizenz gewährten Nutzungsrechte. may exercise further usage rights as specified in the indicated licence. www.econstor.eu Discussion Papers No. 598, December 2009 Statistics Norway, Statistical Methods and Standards Li-Chun Zhang A unit-error theory for register- based household statistics Abstract: The next round of census will be completely register-based in all the Nordic countries. -
A Guide on Probability Distributions
powered project A guide on probability distributions R-forge distributions Core Team University Year 2008-2009 LATEXpowered Mac OS' TeXShop edited Contents Introduction 4 I Discrete distributions 6 1 Classic discrete distribution 7 2 Not so-common discrete distribution 27 II Continuous distributions 34 3 Finite support distribution 35 4 The Gaussian family 47 5 Exponential distribution and its extensions 56 6 Chi-squared's ditribution and related extensions 75 7 Student and related distributions 84 8 Pareto family 88 9 Logistic ditribution and related extensions 108 10 Extrem Value Theory distributions 111 3 4 CONTENTS III Multivariate and generalized distributions 116 11 Generalization of common distributions 117 12 Multivariate distributions 132 13 Misc 134 Conclusion 135 Bibliography 135 A Mathematical tools 138 Introduction This guide is intended to provide a quite exhaustive (at least as I can) view on probability distri- butions. It is constructed in chapters of distribution family with a section for each distribution. Each section focuses on the tryptic: definition - estimation - application. Ultimate bibles for probability distributions are Wimmer & Altmann (1999) which lists 750 univariate discrete distributions and Johnson et al. (1994) which details continuous distributions. In the appendix, we recall the basics of probability distributions as well as \common" mathe- matical functions, cf. section A.2. And for all distribution, we use the following notations • X a random variable following a given distribution, • x a realization of this random variable, • f the density function (if it exists), • F the (cumulative) distribution function, • P (X = k) the mass probability function in k, • M the moment generating function (if it exists), • G the probability generating function (if it exists), • φ the characteristic function (if it exists), Finally all graphics are done the open source statistical software R and its numerous packages available on the Comprehensive R Archive Network (CRAN∗). -
Index English to Chinese
probability A absolutely continuous random variables 绝对连续 随机变 量 analytic theory of probability 解析学概率论 antithetic variables 对 偶变 量 Archimedes 阿基米德 Ars Conjectandi associative law for events (概率)事件结 合律 axioms of probability 概率论 基本公理 axioms of surprise B ballot problem 选 票问题 Banach match problem 巴拿赫火柴问题 basic principle of counting 计 数基本原理 generalized (广义 ) Bayes’s formula ⻉ 叶斯公式 Bernoulli random variables 伯努利随机变 量 Bernoulli trials 伯努利实验 Bernstein polynomials 伯恩斯坦多项 式 Bertand’s paradox ⻉ 特朗悖论 best prize problem beta distribution ⻉ 塔分布 binary symmetric channel binomial coefficents 二项 式系数 binomial random variables 二项 随机变 量 normal approximation 正态 分布逼近 approximation to hypergeometric 超几何分布逼近 computing its mass function 计 算分布函数 moments of 矩(动 差) simulation of 模拟 sums of independent 独立 二项 随机变 量 之和 binomial theorem 二项 式定理 birthday problem 生日问题 probability bivariate exponential distribution 二元指数分布 bivariate normal distribution 二元正态 分布 Bonferroni’s inequality 邦费罗 尼不等式 Boole’s inequality 布尔不等式 Box-Muller simulation technique Box-Muller模拟 Branching process 分支过 程 Buffon’s needle problem 普丰投针问题 C Cantor distribution 康托尔分布 Cauchy distribution 柯西分布 Cauchy - Schwarz inequality 柯西-施瓦兹不等式 center of gravity 重心 central limit theorem 中心极限定理 channel capacity 信道容量 Chapman-Kolmogorov equation 查 普曼-科尔莫戈罗 夫等式 Chebychev’s inequality 切比雪夫不等式 Chernoff bound 切诺 夫界 chi-squared distribution 卡方分布 density function 密度函数 relation to gamma distribution 与伽⻢ 函数关系 simulation of 模拟 coding theory 编码 理论 and entropy 熵 combination 组 合 combinatorial analysis 组 合分析 combinatorial identities -
Handbook on Probability Distributions
R powered R-forge project Handbook on probability distributions R-forge distributions Core Team University Year 2009-2010 LATEXpowered Mac OS' TeXShop edited Contents Introduction 4 I Discrete distributions 6 1 Classic discrete distribution 7 2 Not so-common discrete distribution 27 II Continuous distributions 34 3 Finite support distribution 35 4 The Gaussian family 47 5 Exponential distribution and its extensions 56 6 Chi-squared's ditribution and related extensions 75 7 Student and related distributions 84 8 Pareto family 88 9 Logistic distribution and related extensions 108 10 Extrem Value Theory distributions 111 3 4 CONTENTS III Multivariate and generalized distributions 116 11 Generalization of common distributions 117 12 Multivariate distributions 133 13 Misc 135 Conclusion 137 Bibliography 137 A Mathematical tools 141 Introduction This guide is intended to provide a quite exhaustive (at least as I can) view on probability distri- butions. It is constructed in chapters of distribution family with a section for each distribution. Each section focuses on the tryptic: definition - estimation - application. Ultimate bibles for probability distributions are Wimmer & Altmann (1999) which lists 750 univariate discrete distributions and Johnson et al. (1994) which details continuous distributions. In the appendix, we recall the basics of probability distributions as well as \common" mathe- matical functions, cf. section A.2. And for all distribution, we use the following notations • X a random variable following a given distribution, • x a realization of this random variable, • f the density function (if it exists), • F the (cumulative) distribution function, • P (X = k) the mass probability function in k, • M the moment generating function (if it exists), • G the probability generating function (if it exists), • φ the characteristic function (if it exists), Finally all graphics are done the open source statistical software R and its numerous packages available on the Comprehensive R Archive Network (CRAN∗). -
Statistical Units
Statistical units Preface This chapter details the definitions of statistical units applicable in European business statistics, and is part of the online publication European Business Statistics manual. The statistical unit is the entity for which the required statistics are compiled. It is an analytical unit for which statistics are compiled. Statisticians create it by splitting or combining observation units with the help of estimations or imputations in order to supply more detailed and/or homogeneous data than would otherwise be possible. It is important to understand how it differs from the observation unit. The observation unit is the entity on which information is received. The observation unit and analytical unit may sometimes be the same. The reporting unit is the entity from which the recommended data items are collected. It will vary from sector to sector and from country to country, depending on institutional structures, the legal framework for data collection, traditions, national priorities and survey resources. It may or may not correspond to an observation unit and/or an analytical unit. Besides definitions and explanatory notes on each statistical unit, this chapter currently also includes more detailed operational rules. Whether to keep the full text version of the operational rules in this chapter or to include them merely as hyperlink will be investigated. Contents 1. Introduction 2. What's new 3. Enterprise 3.1 Definition 3.2 Explanatory note 3.3 Operational Rules 3.4 Operational rules for Head Offices, Holding Companies and Special Purpose Entities 4. Enterprise group 4.1 Definition 4.2 Explanatory notes 4.3 Additional explanations 4.4 Operational Rules 5. -
A Complete Bibliography of Publications in Statistics and Probability Letters: 2010–2019
A Complete Bibliography of Publications in Statistics and Probability Letters: 2010{2019 Nelson H. F. Beebe University of Utah Department of Mathematics, 110 LCB 155 S 1400 E RM 233 Salt Lake City, UT 84112-0090 USA Tel: +1 801 581 5254 FAX: +1 801 581 4148 E-mail: [email protected], [email protected], [email protected] (Internet) WWW URL: http://www.math.utah.edu/~beebe/ 24 April 2020 Version 1.24 Title word cross-reference (1; 2) [Yan19a]. (α, β)[Kay15].(1; 1) [BO19]. (k1;k2) [UK18]. (n − k +1) [MF15, ZY10a]. (q; d) [BG16]. (X + Y ) [AV13b]. 1 [Hel19, Red17, SC15, Sze10]. 1=2[Efr10].f2g [Sma15, Jac13, JKD15a, JKD15b, Kha14, LM17, MK10, Ose14a, WL11]. 2k [Lu16a, Lu16b]. 2n−p [Yan13]. 2 × 2 [BB12, MBTC13]. 2 × c [SB16]. 3 [CGLN17, HWB10]. 3x + 1 [DP16]. > [HB18]. > 1=2 [DO11a]. [0; 1] [LP19a]. L1 q (` ) [Ose15a]. k [BML14]. r [VCM14]. A [CDS17, MPA12, PPM18, KDW15]. A1 [Ose13a]. α [AH12b, hCyP15, GT14, LT19b, MY14, MU13, Mic11, MM14b, PP14b, Sre12, Yan17b, ZZT17, ZYS19, ZZ13b, ZLC18, ZZ19a]. AR(1) [CL10, Mar16, PPN10]. AR(p) [WN14b]. ARH(1) [RMAL19].´ β [EM10, JW16, Kre18, Poi19, RBY10, Su10, XXY17, YZ16]. β !1[JW16]. 1 C[0; 1] [Mor18]. C[0; 1] [KM19]. C [LL17c]. D [CDS17, DS15, Jac11, KHN12, Sin19, Sma14, ZP12, Dav12b, OA15,¨ Rez15]. 1 2 Ds [KHN12]. D \ L [BS12a]. δ [Ery12, PB15a]. d ≥ 3 [DH19]. DS [WZ17]. d × R [BS19b]. E [DS15, HR19a, JKD14, KDW15]. E(fNOD)[CKF17].`1 N [AH11]. `1 [Ose19]. `p [Zen14]. [BCD19]. exp(x)[BM11].F [MMM13]. -
Statistical Analysis of Real Manufacturing Process Data Statistické Zpracování Dat Z Reálného Výrobního Procesu
View metadata, citation and similar papers at core.ac.uk brought to you by CORE provided by Digital library of Brno University of Technology BRNO UNIVERSITY OF TECHNOLOGY VYSOKÉ U ČENÍ TECHNICKÉ V BRN Ě FACULTY OF MECHANICAL ENGINEERING DEPARTMENT OF MATHEMATICS FAKULTA STROJNÍHO INŽENÝRSTVÍ ÚSTAV MATEMATIKY STATISTICAL ANALYSIS OF REAL MANUFACTURING PROCESS DATA STATISTICKÉ ZPRACOVÁNÍ DAT Z REÁLNÉHO VÝROBNÍHO PROCESU MASTER’S THESIS DIPLOMOVÁ PRÁCE AUTHOR Bc. BARBORA KU ČEROVÁ AUTOR PRÁCE SUPERVISOR Ing. JOSEF BEDNÁ Ř, Ph.D VEDOUCÍ PRÁCE BRNO 2012 Vysoké učení technické v Brně, Fakulta strojního inženýrství Ústav matematiky Akademický rok: 2011/2012 ZADÁNÍ DIPLOMOVÉ PRÁCE student(ka): Bc. Barbora Kučerová který/která studuje v magisterském navazujícím studijním programu obor: Matematické inženýrství (3901T021) Ředitel ústavu Vám v souladu se zákonem č.111/1998 o vysokých školách a se Studijním a zkušebním řádem VUT v Brně určuje následující téma diplomové práce: Statistické zpracování dat z reálného výrobního procesu v anglickém jazyce: Statistical analysis of real manufacturing process data Stručná charakteristika problematiky úkolu: Statistické zpracování dat z konkrétního výrobního procesu, předpokládá se využití testování hypotéz, metody ANOVA a GLM, analýzy způsobilosti procesu. Cíle diplomové práce: 1. Popis metodologie určování způsobilosti procesu. 2. Stručná formulace konkrétního problému z technické praxe (určení dominantních faktorů ovlivňujících způsobilost procesu). 3. Popis statistických nástrojů vhodných k řešení problému. 4. Řešení popsaného problému s využitím statistických nástrojů. Seznam odborné literatury: 1. Meloun, M., Militký, J.: Kompendium statistického zpracování dat. Academica, Praha, 2002. 2. Kupka, K.: Statistické řízení jakosti. TriloByte, 1998. 3. Montgomery, D.,C.: Introduction to Statistical Quality Control. Chapman Vedoucí diplomové práce: Ing. -
A Guide to Writing a Good Codebook for Data Analysis Projects in Medicine
Codebook cookbook A guide to writing a good codebook for data analysis projects in medicine 1. Introduction Writing a codebook is an important step in the management of any data analysis project. The codebook will serve as a reference for the clinical team; it will help newcomers to the project to rapidly have a flavor of what is at stake and will serve as a communication tool with the statistical unit. Indeed, when comes time to perform statistical analyses on your data, the statistician will be grateful to have a codebook that is readily usable, that is, a codebook that is easy to turn into code for whichever statistical analysis package he/she will use (SAS, R, Stata, or other). 2. Data preparation Whether you enter data in a spreadsheet such as Excel (as is currently popular in biomedical research) or a database program such as Access, there is much freedom in the way data can be entered. A few rules, however, should be followed, to make both the data entry and subsequent data analysis as smooth as possible. A specific example will be presented in Section 3, but first let‟s look at a few general suggestions. 2.1 Variables names A unique, unambiguous name should be given to each variable. Variables names MUST consist of one string only, consisting of letters and — when useful — numbers and underscores ( _ ). Spaces are not allowed in variables names in most statistical programs, even if data entry programs like Excel or Access will allow this. It is good practice to enter variables names at the top of each column. -
Statistical Units in the System of National Accounts
STATISTICAL UNITS IN THE SYSTEM OF NATIONAL ACCOUNTS Group of Experts on National Accounts Geneva, May 18 – 20, 2016 Jennifer Ribarsky Head of Section, OECD Introduction (1/2) • SNA 2008 distinguishes two different types of statistical units: – Establishment in supply and use tables – Institutional units in institutional sector accounts • Call for (re)considering statistical units, also call for possibly reconsidering classifications by industry • Going beyond the present standards of the 2008 SNA • Establishment of a Task Force on Statistical Units, looking at it from a more fundamental point of view 2 Introduction (2/2) SNA 2008, para. A4.21: “At the present there are two reasons to have the concept of establishment within the SNA. The first of these is to provide a link to source information when this is collected on an establishment basis. In cases where basic information is collected on an enterprise basis, this reason disappears. The second reason is for use in input-output tables. Historically, the rationale was to have a unit that related as far as possible to only one activity in only one location so that the link to the physical processes of production was as clear as possible. With the change of emphasis from the physical view of input-output to an economic view, and from product-by-product matrices to industry-by-industry ones, it is less clear that it is essential to retain the concept of establishment in the SNA”. 3 Why are establishments the preferred unit in the SNA 2008? • The premise in the SNA is that the establishment -
Glossary of Terms
Frascati Manual 2015 Guidelines for Collecting and Reporting Data on Research and Experimental Development © OECD 2015 ANNEX 2 Glossary of terms Accounting on an accruals basis recognises a transaction when the activity (decision) generating revenue or consuming resources takes place, regardless of when the associated cash is received or paid. See also accounting on a cash basis. Applied research is original investigation undertaken in order to acquire new knowledge. It is, however, directed primarily towards a specific, practical aim or objective. Appropriations are policy bills that provide / set aside money for specific government departments, agencies, programmes and/or functions. Appropria- tions provide legal authority to enter into obligations that will result in outlays. See also obligations and outlays. Authorisations are policy bills that establish, continue or modify govern- ment programmes and are often accompanied by spending ceilings or policy guidance for subsequent appropriations. However, an authorised funding level has no necessary link with an appropriated funding level. See also appropriations. Basic research is experimental or theoretical work undertaken primarily to acquire new knowledge of the underlying foundations of phenomena and observable facts, without any particular application or use in view. A branch campus abroad (BCA) is defined as a tertiary educational institution that is owned, at least in part, by a local higher education institution (i.e. resident inside the compiling country) but is located in the Rest of the world (resident outside the compiling country); operates in the name of the local higher education institution; engages in at least some face-to-face teaching; and provides access to an entire academic programme that leads to a credential awarded by the local higher education institution. -
List of Glossary Terms
List of Glossary Terms A Ageofaclusterorfuzzyrule 1054 A correlated equilibrium 3064 Agent 58, 76, 105, 1767, 2578, 3004 Amechanism 1837 Agent architecture 105 Anearestneighbor 790 Agent based models 2940 A social choice function 1837 Agent (or software agent) 2999 A stochastic game 3064 Agent-based computational models 2898 Astrategy 3064 Agent-based model 58 Abelian group 2780 Agent-based modeling 1767 Absolute temperature 940 Agent-based modeling (ABM) 39 Absorbing state 1080 Agent-based simulation 18, 88 Abstract game 675 Aggregation 862 Abstract game of network formation with respect to Aggregation operators 122 irreflexive dominance 2044 Aging 2564, 2611 Abstract game of network formation with respect to path Algebra 2925 dominance 2044 Algebraic models 2898 Accuracy 161, 827 Algorithm 2496 Accuracy (rate) 862 Algorithmic complexity of object x 132 Achievable mate 3235 Algorithmic self-assembly of DNA tiles 1894 Action profile 3023 Allowable set of partners 3234 Action set 3023 Almost equicontinuous CA 914, 3212 Action type 2656 Alphabet of a cellular automaton 1 Activation function 813 ˛-Level set and support 1240 Activator 622 Alternating independent-2-paths 2953 Active membranes 1851 Alternating k-stars 2953 Actors 2029 Alternating k-triangles 2953 Adaptation 39 Amorphous computer 147 Adaptive system 1619 Analog 3187 Additive cellular automata 1 Analog circuit 3260 Additively separable preferences 3235 Ancilla qubits 2478 Adiabatic switching 1998 Anisotropic elements 754 Adjacency matrix 1746, 3114 Annealed law 2564 Adjacent 2864,