I n many cases, the SPRT will come to a decision with fewer samples than would have been required for a fixed size test.
|
|
- Ashley Hardy
- 6 years ago
- Views:
Transcription
1 STATGRAPHICS Rev. 9/6/3 Sequential Saling Suary... Data Inut... 3 Analysis Otions... 3 Analysis Suary... 5 Cuulative Plot... 6 Decision Nubers... 9 Test Perforance... O. C. Curve... ASN Function... Forulas... Suary The Sequential Saling rocedure ileents various Sequential Probability Ratio Tests (SPRTs). Unlie statistical tests which have a fixed sale size, the nuber of sales required by sequential tests is not redeterined. Instead, after each sale is taen, one of 3 decisions is ade:. Sto the test and reject the null hyothesis.. Sto the test and accet the null hyothesis. 3. Continue saling. I n any cases, the SPRT will coe to a decision with fewer sales than would have been required for a fixed size test. Sale StatFolio: wald.sg 3 by StatPoint Technologies, Inc. Sequential Saling -
2 Sale Data: STATGRAPHICS Rev. 9/6/3 The file wald.sgd contains a set of observations resented in the classic boo by Wald (947). The data are shown in the following table: Sale Measureent It is assued that the data are rando sales fro a noral distribution with nown standard deviation = 5. It is desired to test the following hyotheses concerning the ean of the distribution: Null hyothesis: = 35 Alternative hyothesis: = 5 3 by StatPoint Technologies, Inc. Sequential Saling -
3 STATGRAPHICS Rev. 9/6/3 Data Inut The data inut dialog box requests the nae of the colun containing the easureents: Data variable: an otional colun containing the data that has been collected so far. If no data has yet been collected, this field ay be left blan. Select: subset selection. Analysis Otions After the data has been secified, the Analysis Otions dialog box is dislayed in order to secify the test to be conducted: 3 by StatPoint Technologies, Inc. Sequential Saling - 3
4 STATGRAPHICS Rev. 9/6/3 Test araeter: the araeter whose value is secified by the null and alternative hyotheses. Otions are: o Noral ean (siga nown): the ean of a noral distribution, assuing that the value of the standard deviation is nown. The value of siga ust be secified. o Noral ean (siga unnown): the ean of a noral distribution, assuing that the value of the standard deviation is not nown. The value of siga will be estiated fro the data. o Noral siga (ean nown): the standard deviation of a noral distribution, assuing that the value of the ean is nown. The value of the ean ust be secified. o Noral siga (ean unnown): the standard deviation of a noral distribution, assuing that the value of the ean is not nown. The value of the ean will be estiated fro the data. 3 by StatPoint Technologies, Inc. Sequential Saling - 4
5 STATGRAPHICS Rev. 9/6/3 o Binoial roortion : the robability of an event for the binoial distribution. If this otion is selected, all data values ust be either or. o Poisson rate : the rate araeter or ean of the Poisson distribution. If this otion is selected, all data values ust be non-negative integers. o Negative binoial ean: the ean of the negative binoial distribution. If this otion is selected, all data values ust be non-negative integers. This distribution is often used in lace of the Poisson distribution for overdisersed counts (counts for which the variance is greater than the ean). The value of the araeter, (an integer greater than or equal to ), ust be secified. Null hyothesis H: the value of the araeter secified by the null hyothesis. Null hyothesis alha ris: the -ris of the test. This is the robability that the test will end with an incorrect rejection of the null hyothesis. Alternative hyothesis H: the value of the araeter secified by the alternative hyothesis. Alternative hyothesis beta ris: the -ris of the test. This is the robability that the test will end with an incorrect accetance of the null hyothesis. Two-sided test: whether the test is two-sided or one-sided (available when testing the noral ean only). If a two-sided test is selected, the alternative hyothesis is assued to consist of two values equally saced around the null hyothesis. Analysis Suary The Analysis Suary dislays sale statistics for the data (if any), a suary of the hyothesis test, and the status of the test: Sequential Analysis (One Sale) Data variable: Measureent Count Average 33.5 Median 37.5 Standard deviation 6.49 Miniu 4. Maxiu 6. Stnd. sewness Stnd. Kurtosis Hyothesis Test Null hyothesis ean = 35. alha ris =. Alternative hyothesis ean = 5. beta ris =.3 Known siga = 5. Decision: reject null hyothesis at sale After each data value, a decision is ade to accet or reject the null hyothesis if there is sufficient inforation to sto the test. Otherwise, the rogra will indicate that continued 3 by StatPoint Technologies, Inc. Sequential Saling - 5
6 Cuulative su STATGRAPHICS Rev. 9/6/3 saling (ore data) is required. Tyically, observations are collected and added to the data colun one at a tie until enough data have been collected to sto the test. Cuulative Plot The Cuulative Plot illustrates the decision regions aroriate for a sequential saling lan. For the current exale, it lots the cuulative su of the data values after sales have been taen. Let X i be the observed data value for the i-th sale. Then the cuulative su is defined by X i i () In the lot below, these sus are lotted versus sale nuber: (X ) 4 Sequential Saling Plot for Measureent Sale Also included on the lot are three regions:. A rejection region, colored red. If the cuulative su enters the rejection region, saling is stoed and the null hyothesis is rejected.. An accetance region, colored green. If the cuulative su enters the accetance region, saling is stoed and the null hyothesis is acceted. 3. A white region. As long as the cuulative su reains in this region, additional sales ust be collected. The oint at which the statistic first enters the accetance or rejection region, at which further data collection would sto, is indicated by a red asteris. For the exale data, saling would 3 by StatPoint Technologies, Inc. Sequential Saling - 6
7 Cuulative deviation STATGRAPHICS Rev. 9/6/3 continue until = sales have been collected, at which tie the null hyothesis would be acceted. If a two-sided test of were selected, the lot would show instead the cuulative su of deviations fro the null hyothesis, defined by X i i () The rejection region then consist of two sections, one for alternative hyotheses greater than the null and another for alternative hyotheses less than the null. A tyical exale is shown below: Sequential Saling Plot for Measureent Sale In the lot above, saling would continue beyond =, since all of the oints are within the white zone. If the value of siga was not nown, the SPRT erfored would be a sequential t-test. In such a test, the quantity lotted is the log lielihood ratio defined by t t f, (3) f, where t is the standard t-statistic calculated fro the sale ean and sale standard deviation x t (4) s / 3 by StatPoint Technologies, Inc. Sequential Saling - 7
8 Log lielihood ratio STATGRAPHICS Rev. 9/6/3 and f(t,) is the robability density function for the non-central t-distribution with degrees of freedo and non-centrality araeter (5) s / n The decision liits are then two arallel lines as shown below: Sequential Saling Plot - Measureent Sale In the lot above, the null hyothesis would have been acceted after 6 sales were collected, since the log lielihood ratio enters the green accetance zone at that oint. Pane Otions Miniu n to dislay: The lot can be extended beyond the end of the data by secifying a larger sale size. 3 by StatPoint Technologies, Inc. Sequential Saling - 8
9 STATGRAPHICS Rev. 9/6/3 Decision Nubers This table dislays the value of the lotted statistic and corresonding accetance and rejection values after each sale is collected: Decision Nubers Accetance Rejection Sale Cuulative su Nuber Nuber If the data leads to accetance or rejection of the null hyothesis, the value of the statistic at which that occurs is shown in red. Pane Otions Miniu n to dislay: The table can be extended beyond the end of the data by secifying a larger sale size. 3 by StatPoint Technologies, Inc. Sequential Saling - 9
10 STATGRAPHICS Rev. 9/6/3 Test Perforance The Test Perforance table dislays the roerties of the sequential test: Test Perforance Mean Prob(accet null) Average sale nuber Null hy Alt. hy Sale size for fixed test n = 5 The table has rows for various values of the araeter being tested. Two iortant quantities are dislayed:. Prob(accet null) This is the robability that the test will lead to accetance of the null hyothesis. The alha and beta ris set this robability when the ean exactly equals the null and alternative hyotheses, resectively. At values between the null and the alternative, the robability will vary.. Average sale nuber The average nuber of sales that ust be taen before the test either accets or rejects the null hyothesis. The table also shows the sale size required by a test with redeterined sale size. In ost cases, the average sale size for the sequential test will be saller than for the fixed size test. O. C. Curve The O. C. Curve or Oerating Characteristic Curve lots the robability that the null hyothesis will be acceted versus various values of the araeter being tested: 3 by StatPoint Technologies, Inc. Sequential Saling -
11 Average sale nuber Prob(accet null) STATGRAPHICS Rev. 9/6/3 OC Curve Mean This curve asses through the oints (, ) and (,). ASN Function The ASN Function or Average Sale Nuber Function lots the average sale size required before the null hyothesis is either acceted or rejected as a function of the true value of the araeter being tested: ASN Function Mean A horizontal line is also dislayed at the sale size required for a fixed size test. 3 by StatPoint Technologies, Inc. Sequential Saling -
12 Forulas STATGRAPHICS Rev. 9/6/3 Test of Noral Mean (One-Sided, Siga Known) Test statistic: X i i (6) Accetance nuber: (7) Rejection nuber: (8) Test of Noral Mean (Two-Sided, Siga Known) Test statistic: X i i (9) Accetance nuber: / () Rejection nuber: / () where () Test of Noral Mean (One-Sided, Siga Unnown) Test statistic: t t f, (3) f, Accetance nuber: T (4) Rejection nuber: T (5) 3 by StatPoint Technologies, Inc. Sequential Saling -
13 Test of Noral Mean (Two-Sided, Siga Unnown) STATGRAPHICS Rev. 9/6/3 Test statistic: t t f, (6) f, Accetance nuber: T (7) / Rejection nuber: T (8) / Test of Noral Siga (Mean Known) Test statistic: X i i (9) Accetance nuber: () Rejection nuber: () Test of Noral Siga (Mean Unnown) This case uses the sae statistic as the test which assues that the ean is nown. The accetance and rejection nubers after sales have been collected corresond to the accetance and rejection nubers after - sales when the ean is nown. Test of Binoial Proortion Test statistic: X i i () 3 by StatPoint Technologies, Inc. Sequential Saling - 3
14 STATGRAPHICS Rev. 9/6/3 3 by StatPoint Technologies, Inc. Sequential Saling - 4 Accetance nuber: (3) Rejection nuber: (4) Test of Poisson Rate Test statistic: i X i (5) Accetance nuber: (6) Rejection nuber: (7) Test of Negative Binoial Mean Test statistic: i X i (8) Accetance nuber: (9)
15 STATGRAPHICS Rev. 9/6/3 3 by StatPoint Technologies, Inc. Sequential Saling - 5 Rejection nuber: (3) Oerating Characteristic Curve h h h L ) ( (3) where h() is a quantity that deends on the value of the unnown araeter (see Wald, 947). Average Sale Nuber,, ) ( ) ( ) ( x f x f E L L n E (3) where f(x,) is the robability density or ass function for the data variable.
EXTENDED SVD FLATNESS CONTROL. Per Erik Modén and Markus Holm ABB AB, Västerås, Sweden
EXTENDED SVD FLATNESS CONTROL Per Erik Modén and Markus Hol ABB AB, Västerås, Sweden ABSTRACT Cold rolling ills soeties do not see able to control flatness as well as exected, taking into account the nuber
More informationNew method of angle error measurement in angular artifacts using minimum zone flatness plane
Alied Mechanics and Materials Subitted: 04-05-4 ISSN: 66-748, Vols. 599-60, 997-004 Acceted: 04-06-05 doi:0.408/www.scientific.net/amm.599-60.997 Online: 04-08- 04 Trans Tech Publications, Switzerland
More informationMultivariate Capability Analysis
Multivariate Capability Analysis Summary... 1 Data Input... 3 Analysis Summary... 4 Capability Plot... 5 Capability Indices... 6 Capability Ellipse... 7 Correlation Matrix... 8 Tests for Normality... 8
More informationMoving Average (MA) Charts
Moving Average (MA) Charts Summary The Moving Average Charts procedure creates control charts for a single numeric variable where the data have been collected either individually or in subgroups. In contrast
More informationProcess and Measurement System Capability Analysis
Process and Measurement System aability Analysis Process caability is the uniformity of the rocess. Variability is a measure of the uniformity of outut. Assume that a rocess involves a quality characteristic
More informationA Fail-Aware Datagram Service
A Fail-Aware Datagra Service Christof Fetzer and Flaviu Cristian christof@research.att.co, htt://www.christof.org Abstract In distributed real-tie systes it is often useful for a rocess to know that another
More informationELEVATION SURFACE INTERPOLATION OF POINT DATA USING DIFFERENT TECHNIQUES A GIS APPROACH
ELEVATION SURFACE INTERPOLATION OF POINT DATA USING DIFFERENT TECHNIQUES A GIS APPROACH Kulapraote Prathuchai Geoinforatics Center, Asian Institute of Technology, 58 Moo9, Klong Luang, Pathuthani, Thailand.
More informationRecap Consistent cuts. CS514: Intermediate Course in Operating Systems. What time is it? But what does time mean? Drawing time-line pictures:
CS514: Interediate Course in Oerating Systes Professor Ken iran Vivek Vishnuurthy: T Reca Consistent cuts On Monday we saw that sily gathering the state of a syste isn t enough Often the state includes
More informationPERFORMANCE MEASURES FOR INTERNET SERVER BY USING M/M/m QUEUEING MODEL
IJRET: International Journal of Research in Engineering and Technology ISSN: 239-63 PERFORMANCE MEASURES FOR INTERNET SERVER BY USING M/M/ QUEUEING MODEL Raghunath Y. T. N. V, A. S. Sravani 2 Assistant
More informationScreening Design Selection
Screening Design Selection Summary... 1 Data Input... 2 Analysis Summary... 5 Power Curve... 7 Calculations... 7 Summary The STATGRAPHICS experimental design section can create a wide variety of designs
More informationMeasuring Bottleneck Bandwidth of Targeted Path Segments
Measuring Bottleneck Bandwidth of Targeted Path Segents Khaled Harfoush Deartent of Couter Science North Carolina State University Raleigh, NC 27695 harfoush@cs.ncsu.edu Azer Bestavros Couter Science Deartent
More informationAnalysis of a Biologically-Inspired System for Real-time Object Recognition
Cognitive Science Online, Vol.3.,.-4, 5 htt://cogsci-online.ucsd.edu Analysis of a Biologically-Insired Syste for Real-tie Object Recognition Erik Murhy-Chutorian,*, Sarah Aboutalib & Jochen Triesch,3
More informationcmd p p p cmd cmd ... cmd p p p command 1 command 2 command 3 cmd cmd ... cmd S M T W T F S command n cmd ... cmdn p p p cmd ...
The Trials and Tribulations of Building an Adative User Interface Benjain Korveaker & Russell Greiner fbenjain, greinerg@cs.ualberta.ca Deartent of Couter Science University of Alberta Edonton, Canada
More informationEfficient Constraint Evaluation Algorithms for Hierarchical Next-Best-View Planning
Efficient Constraint Evaluation Algoriths for Hierarchical Next-Best-View Planning Ko-Li Low National University of Singapore lowl@cop.nus.edu.sg Anselo Lastra University of North Carolina at Chapel Hill
More informationClustering. Cluster Analysis of Microarray Data. Microarray Data for Clustering. Data for Clustering
Clustering Cluster Analysis of Microarray Data 4/3/009 Copyright 009 Dan Nettleton Group obects that are siilar to one another together in a cluster. Separate obects that are dissiilar fro each other into
More informationEvaluation of a multi-frame blind deconvolution algorithm using Cramér-Rao bounds
Evaluation of a ulti-frae blind deconvolution algorith using Craér-Rao bounds Charles C. Beckner, Jr. Air Force Research Laboratory, 3550 Aberdeen Ave SE, Kirtland AFB, New Mexico, USA 87117-5776 Charles
More informationDKD-R 4-2 Calibration of Devices and Standards for Roughness Metrology Sheet 2: Calibration of the vertical measuring system of stylus instruments
DKD-R 4- Calibration of Devices and Standards for Roughness Metrology Sheet : Calibration of the vertical easuring syste of stylus instruents DKD-R 4- Sheet.7 DKD-R 4- Calibration of Devices and Standards
More informationIf the active datasheet is empty when the StatWizard appears, a dialog box is displayed to assist in entering data.
StatWizard Summary The StatWizard is designed to serve several functions: 1. It assists new users in entering data to be analyzed. 2. It provides a search facility to help locate desired statistical procedures.
More informationOn-Chip Interconnect Implications of Shared Memory Multicores
On-Chi Interconnect Ilications of Shared Meory Multicores Srini Devadas Couter Science and Artificial Intelligence Laboratory (CSAIL) Massachusetts Institute of Technology 1 Prograing 1000 cores MPI has
More informationFeatures. 210 mm. relay outputs 64 mm. input. triac outputs
12 Mark Terinal Unit Controller (3 fan seed ) 12 LONMARK TERMINAL UNIT CONTROLLER (3 fan seed) Descrition The 12 is a terinal unit controller which can be networked using Works (). It can counicate with
More informationSmarter Balanced Assessment Consortium Claims, Targets, and Standard Alignment for Math
Sarter Balanced Assessent Consortiu s, s, Stard Alignent for Math The Sarter Balanced Assessent Consortiu (SBAC) has created a hierarchy coprised of clais targets that together can be used to ake stateents
More informationFlucs: Artificial Lighting & Daylighting. IES Virtual Environment
Flucs: Artificial Lighting & Daylighting IES Virtual Environent Contents 1. General Description of the FLUCS Interface... 6 1.1. Coon Controls... 6 1.2. Main Application Window... 6 1.3. Other Windows...
More informationTrees. Linear vs. Branching CSE 143. Branching Structures in CS. What s in a Node? A Tree. [Chapter 10]
CSE 143 Trees [Chapter 10] Linear vs. Branching Our data structures so far are linear Have a beginning and an end Everything falls in order between the ends Arrays, lined lists, queues, stacs, priority
More informationA Fail-Aware Datagram Service
A Fail-Aware Datagra Service Christof Fetzer and Flaviu Cristian christof@research.att.co, htt://www.christof.org Abstract In distributed real-tie systes it is often useful for a rocess Ô to know that
More informationKS3 Maths Progress Delta 2-year Scheme of Work
KS3 Maths Progress Delta 2-year Schee of Work See the spreadsheet for the detail. Essentially this is an express route through our higher-ability KS3 Maths Progress student books Delta 1, 2 and 3 as follows:
More information1 Extended Boolean Model
1 EXTENDED BOOLEAN MODEL It has been well-known that the Boolean odel is too inflexible, requiring skilful use of Boolean operators to obtain good results. On the other hand, the vector space odel is flexible
More informationHomework 1. An Introduction to Neural Networks
Hoework An Introduction to Neural Networks -785: Introduction to Deep Learning Spring 09 OUT: January 4, 09 DUE: February 6, 09, :59 PM Start Here Collaboration policy: You are expected to coply with the
More informationA Preliminary Study of Maximum System-level Crosstalk at High Frequencies for Coupled Transmission Lines
A Preliinary tudy of Maxiu yste-leel Crosstalk at High Frequencies for Couled Transission Lines iaoeng Dong, Haixiao Weng, Daryl G Beetner Todd Hubing Electroagnetic Coatibility Laboratory Uniersity of
More informationDevelopment of an Integrated Cost Estimation and Cost Control System for Construction Projects
ABSTRACT Developent of an Integrated Estiation and Control Syste for Construction s by Salan Azhar, Syed M. Ahed and Aaury A. Caballero Florida International University 0555 W. Flagler Street, Miai, Florida
More informationTrigonometric Functions
Similar Right-Angled Triangles Trigonometric Functions We lan to introduce trigonometric functions as certain ratios determined by similar right-angled triangles. By de nition, two given geometric gures
More informationA Novel Architecture for Compiled-type Software CNC System
Key Engineering Materials Online: 2007-05-15 ISSN: 1662-9795, ol. 339, 442-446 doi:10.4028/.scientific.net/kem.339.442 2007 rans ech Pulications, Sitzerland A Novel Architecture for Coiled-tye Softare
More informationGroup MRF for fmri Activation Detection
Grou MRF for fmri Activation Detection Bernard Ng, Rafeef Abugharbieh The University of British Colubia 9 West Mall, Vancouver, BC VT Z bernardn@ece.ubc.ca, rafeef@ece.ubc.ca htt://bisicl.ece.ubc.ca Ghassan
More informationAn Ensemble of Adaptive Neuro-Fuzzy Kohonen Networks for Online Data Stream Fuzzy Clustering
An Enseble of Adative euro-fuzzy Kohonen etworks for Online Data Strea Fuzzy Clustering Zhengbing Hu School of Educational Inforation Technology Central China oral University Wuhan China Eail: hzb@ail.ccnu.edu.cn
More informationTwo hours UNIVERSITY OF MANCHESTER. January Answer any THREE questions. Answer each question in a separate book
Two hours UNIVERSITY OF MANCHESTER Database Architecture Models and Design January 2004 Answer any THREE questions Answer each question in a separate book The use of electronic calculators is not peritted.
More informationMarkov Analysis for Optimum Caching as an Alternative to Belady s Algorithm
arov Analysis for Otiu Caching as an Alternative to Belady s Algorith, Deutsche Teleo, Darstadt, Gerany gerhard.hasslinger@teleo.de Analytic Results on LRU, LFU, Otiu Caching Belady s Princile for Otiu
More informationImproving Trust Estimates in Planning Domains with Rare Failure Events
Imroving Trust Estimates in Planning Domains with Rare Failure Events Colin M. Potts and Kurt D. Krebsbach Det. of Mathematics and Comuter Science Lawrence University Aleton, Wisconsin 54911 USA {colin.m.otts,
More informationReal-Time Detection of Invisible Spreaders
Real-Tie Detection of Invisible Spreaders MyungKeun Yoon Shigang Chen Departent of Coputer & Inforation Science & Engineering University of Florida, Gainesville, FL 3, USA {yoon, sgchen}@cise.ufl.edu Abstract
More informationInteractive Fuzzy Modeling by Evolutionary Multiobjective Optimization with User Preference
Interactive Fuzzy Modeling by Evolutionary Multiobjective Otiization with User Preference Yusue ojia, Hisao Ishibuchi Deartent of Couter Science and Intelligent Systes, Osaa Prefecture University - Gauen-cho,
More informationMedical Biophysics 302E/335G/ st1-07 page 1
Medical Biophysics 302E/335G/500 20070109 st1-07 page 1 STEREOLOGICAL METHODS - CONCEPTS Upon copletion of this lesson, the student should be able to: -define the ter stereology -distinguish between quantitative
More informationReconstruction of Time Series using Optimal Ordering of ICA Components
Reconstruction of Tie Series using Optial Ordering of ICA Coponents Ar Goneid and Abear Kael Departent of Coputer Science & Engineering, The Aerican University in Cairo, Cairo, Egypt e-ail: goneid@aucegypt.edu
More informationCOMP 250. Lecture 4. Array lists. Sept. 15, 2017
COMP 25 Lecture 4 Arra lists Set. 5, 27 Arras in Java int[ ] Ints = new int[5]; Ints[3] = -732; Arra whose eleents have a riitive te 2 Ints int[ ] Ints = new int[5]; Ints[3] = -732; 2 3 : 4-732 : Arras
More informationpart 3 Martin Samuelčík Room I4
art 3 Martin Sauelčík htt://www.sccg.sk/~sauelcik Roo I4 Vertex coordinates Fro inut coordinates to window coordinates Coordinates are always related to coordinates syste, sace, frae Transforing vertex
More informationMultivariate Normal Random Numbers
Multivariate Normal Random Numbers Revised: 10/11/2017 Summary... 1 Data Input... 3 Analysis Options... 4 Analysis Summary... 5 Matrix Plot... 6 Save Results... 8 Calculations... 9 Summary This procedure
More informationPredicting x86 Program Runtime for Intel Processor
Predicting x86 Progra Runtie for Intel Processor Behra Mistree, Haidreza Haki Javadi, Oid Mashayekhi Stanford University bistree,hrhaki,oid@stanford.edu 1 Introduction Progras can be equivalent: given
More informationPerformance analysis of hybrid (M/M/1 and M/M/m) client server model using Queuing theory
International Journal of Electronic and Couter cience Engineering vailable Online at wwwijeceorg IN- 77-9 erforance analyi of hybrid M/M/ and M/M/ client erver odel uing ueuing theory atarhi Guta, Dr Rajan
More informationThe Application of Bandwidth Optimization Technique in SLA Negotiation Process
The Application of Bandwidth Optiization Technique in SLA egotiation Process Srećko Krile University of Dubrovnik Departent of Electrical Engineering and Coputing Cira Carica 4, 20000 Dubrovnik, Croatia
More informationλ-harmonious Graph Colouring Lauren DeDieu
λ-haronious Graph Colouring Lauren DeDieu June 12, 2012 ABSTRACT In 198, Hopcroft and Krishnaoorthy defined a new type of graph colouring called haronious colouring. Haronious colouring is a proper vertex
More information1. Here is the power function for a particular test about p = fraction of beverage orders for Coke in a restaurant. Suppose that p 0 = 0.3.
Recitation Assignment due 12-8-09. Exam 5 coverage: Most of the exam questions go to material from chaters 16 and 20, but some may touch on suorting material from chater 15. Exect to see questions relating
More informationNON-RIGID OBJECT TRACKING: A PREDICTIVE VECTORIAL MODEL APPROACH
NON-RIGID OBJECT TRACKING: A PREDICTIVE VECTORIAL MODEL APPROACH V. Atienza; J.M. Valiente and G. Andreu Departaento de Ingeniería de Sisteas, Coputadores y Autoática Universidad Politécnica de Valencia.
More informationPackage RSSampling. March 19, 2018
Type Package Title Ranked Set Sapling Version 1.0 Package RSSapling March 19, 2018 Author Busra Sevinc, Bekir Cetintav, Melek Eseen, Sela Gurler Maintainer Busra Sevinc Iports LearnBayes,
More informationMIDA: AN IDA SEARCH WITH DYNAMIC CONTROL
April 1991 UILU-ENG-91-2216 CRHC-91-9 Center fo r Reliable and High-Perforance Coputing MIDA: AN IDA SEARCH WITH DYNAMIC CONTROL Benjain W. Wah Coordinated Science Laboratory College of Engineering UNIVERSITY
More informationChapter 7b - Point Estimation and Sampling Distributions
Chater 7b - Point Estimation and Samling Distributions Chater 7 (b) Point Estimation and Samling Distributions Point estimation is a form of statistical inference. In oint estimation we use the data from
More informationModal Masses Estimation in OMA by a Consecutive Mass Change Method.
Modal Masses Estiation in OMA by a Consecutive Mass Change Method. F. Pelayo University of Oviedo, Departent of Construction and Manufacturing Engineering, Gijón, Spain M. López-Aenlle University of Oviedo,
More informationDerivation of an Analytical Model for Evaluating the Performance of a Multi- Queue Nodes Network Router
Derivation of an Analytical Model for Evaluating the Perforance of a Multi- Queue Nodes Network Router 1 Hussein Al-Bahadili, 1 Jafar Ababneh, and 2 Fadi Thabtah 1 Coputer Inforation Systes Faculty of
More informationSmarter Balanced Assessment Consortium Claims, Targets, and Standard Alignment for Math
Sarter Balanced Assessent Consortiu Clais, s, Stard Alignent for Math The Sarter Balanced Assessent Consortiu (SBAC) has created a hierarchy coprised of clais targets that together can be used to ake stateents
More informationThe Horizontal Deformation Analysis of High-rise Buildings
Environental Engineering 10th International Conference eissn 2029-7092 / eisbn 978-609-476-044-0 Vilnius Gediinas Technical University Lithuania, 27 28 April 2017 Article ID: enviro.2017.194 http://enviro.vgtu.lt
More informationCollection Selection Based on Historical Performance for Efficient Processing
Collection Selection Based on Historical Perforance for Efficient Processing Christopher T. Fallen and Gregory B. Newby Arctic Region Supercoputing Center University of Alaska Fairbanks Fairbanks, Alaska
More informationSurvivability Function A Measure of Disaster-Based Routing Performance
Survivability Function A Measure of Disaster-Based Routing Perforance Journal Club Presentation on W. Molisz. Survivability function-a easure of disaster-based routing perforance. IEEE Journal on Selected
More informationTALLINN UNIVERSITY OF TECHNOLOGY, INSTITUTE OF PHYSICS 17. FRESNEL DIFFRACTION ON A ROUND APERTURE
7. FRESNEL DIFFRACTION ON A ROUND APERTURE. Objective Exaining diffraction pattern on a round aperture, deterining wavelength of light source.. Equipent needed Optical workbench, light source, color filters,
More informationEfficient Estimation of Inclusion Coefficient using HyperLogLog Sketches
Efficient Estiation of Inclusion Coefficient using HyperLogLog Sketches Azade Nazi, Bolin Ding, Vivek Narasayya, Surajit Chaudhuri Microsoft Research {aznazi, bolind, viveknar, surajitc}@icrosoft.co ABSTRACT
More informationTHE problem of transferring a data file or data stream. Reliable Point-to-point Underwater Acoustic Data Transfer: To Juggle or Not to Juggle?
1 Reliable Point-to-point Underwater Acoustic Data Transfer: To Juggle or Not to Juggle? Mandar Chitre, Senior Meber, IEEE, and Wee-Seng Soh, Meber, IEEE Abstract Reliable data transfer speeds using underwater
More informationAdaptive Parameter Estimation Based Congestion Avoidance Strategy for DTN
Proceedings of the nd International onference on oputer Science and Electronics Engineering (ISEE 3) Adaptive Paraeter Estiation Based ongestion Avoidance Strategy for DTN Qicai Yang, Futong Qin, Jianquan
More informationShuigeng Zhou. May 18, 2016 School of Computer Science Fudan University
Query Processing Shuigeng Zhou May 18, 2016 School of Comuter Science Fudan University Overview Outline Measures of Query Cost Selection Oeration Sorting Join Oeration Other Oerations Evaluation of Exressions
More informationWeeks 1 3 Weeks 4 6 Unit/Topic Number and Operations in Base 10
Weeks 1 3 Weeks 4 6 Unit/Topic Nuber and Operations in Base 10 FLOYD COUNTY SCHOOLS CURRICULUM RESOURCES Building a Better Future for Every Child - Every Day! Suer 2013 Subject Content: Math Grade 3rd
More informationcm3520 cm3525 Security Function
wwwiagisticsco c3520 c3525 Security Function Contents Contents 1 Security 11 Introduction 1-2 12 Tradearks and Registered Tradearks 1-2 13 Copliance with the ISO15408 Standard 1-2 14 Operating Precautions1-2
More informationNear Light Correction for Image Relighting and 3D Shape Recovery
Near Light Correction for Iage Relighting and 3D Shae Recovery Anonyous for Review Abstract In this aer, we roose a near-light illuination odel for iage relighting and 3D shae recovery Classic ethods such
More informationSolving the Damage Localization Problem in Structural Health Monitoring Using Techniques in Pattern Classification
Solving the Daage Localization Proble in Structural Health Monitoring Using Techniques in Pattern Classification CS 9 Final Project Due Dec. 4, 007 Hae Young Noh, Allen Cheung, Daxia Ge Introduction Structural
More informationBrian Noguchi CS 229 (Fall 05) Project Final Writeup A Hierarchical Application of ICA-based Feature Extraction to Image Classification Brian Noguchi
A Hierarchical Application of ICA-based Feature Etraction to Iage Classification Introduction Iage classification poses one of the greatest challenges in the achine vision and achine learning counities.
More informationTOLLY. Premise: Buyers of multi-port. BayStack T Switch Fast Ethernet Switch Competitive Evaluation Backplane Capacity.
Preise: Buyers of ulti-port switches need to know the actual capacity of the backplane (switching fabric) used to interconnect ports. To assure a non-blocking environent i.e., all ports can run at wire-speed
More informationA DEA-bases Approach for Multi-objective Design of Attribute Acceptance Sampling Plans
Available online at htt://ijdea.srbiau.ac.ir Int. J. Data Enveloment Analysis (ISSN 2345-458X) Vol.5, No.2, Year 2017 Article ID IJDEA-00422, 12 ages Research Article International Journal of Data Enveloment
More informationGeometry. The Method of the Center of Mass (mass points): Solving problems using the Law of Lever (mass points). Menelaus theorem. Pappus theorem.
Noveber 13, 2016 Geoetry. The Method of the enter of Mass (ass points): Solving probles using the Law of Lever (ass points). Menelaus theore. Pappus theore. M d Theore (Law of Lever). Masses (weights)
More informationThe Internal Conflict of a Belief Function
The Internal Conflict of a Belief Function Johan Schubert Abstract In this paper we define and derive an internal conflict of a belief function We decopose the belief function in question into a set of
More informationSmarter Balanced Assessment Consortium Claims, Targets, and Standard Alignment for Math
Sarter Balanced Assessent Consortiu s, s, Stard Alignent for Math The Sarter Balanced Assessent Consortiu (SBAC) has created a hierarchy coprised of clais targets that together can be used to ake stateents
More informationSingle Degree-of-Freedom Rigidly Foldable Cut Origami Flashers
Robert J. Lang Lang Origai, Alao, CA 9507 e-ail: robert@langorigai.co Sencer Magleby Deartent of Mechanical Engineering, Brigha Young University, Provo, UT 860 e-ail: agleby@byu.edu Larry Howell Deartent
More informationCLOUD computing is quickly becoming an effective and
IEEE TRANSACTIONS ON PARALLEL AND DISTRIBUTED SYSTEMS, VOL. 24, NO. 6, JUNE 203 087 Otial Multiserver Configuration for Profit Maxiization in Cloud Couting Junwei Cao, Senior Meber, IEEE, Kai Hwang, Fellow,
More informationSEMI-AUTOMATIC ROAD EXTRACTION FROM HIGH-RESOLUTION SATELLITE IMAGE
SEMI-AUOMAIC ROAD EXRACION FROM HIGH-RESOLUION SAELLIE IMAGE Commission III KEY WORDS: Road Extraction, High-Resolution Satellite Image, Urban area, Semi-automatic ABSRAC: In this research, a method is
More informationShortest Path Determination in a Wireless Packet Switch Network System in University of Calabar Using a Modified Dijkstra s Algorithm
International Journal of Engineering and Technical Research (IJETR) ISSN: 31-869 (O) 454-4698 (P), Volue-5, Issue-1, May 16 Shortest Path Deterination in a Wireless Packet Switch Network Syste in University
More informationA GRAPH-PLANARIZATION ALGORITHM AND ITS APPLICATION TO RANDOM GRAPHS
A GRAPH-PLANARIZATION ALGORITHM AND ITS APPLICATION TO RANDOM GRAPHS T. Ozawa and H. Takahashi Departent of Electrical Engineering Faculty of Engineering, Kyoto University Kyoto, Japan 606 Abstract. In
More informationVodafone MachineLink. Port Forwarding / DMZ Configuration Guide
Vodafone MachineLink Port Forwarding / DMZ Configuration Guide Docuent history This guide covers the following products: Vodafone MachineLink 3G (NWL-10) Vodafone MachineLink 3G Plus (NWL-12) Vodafone
More informationOblivious Routing for Fat-Tree Based System Area Networks with Uncertain Traffic Demands
Oblivious Routing for Fat-Tree Based Syste Area Networks with Uncertain Traffic Deands Xin Yuan Wickus Nienaber Zhenhai Duan Departent of Coputer Science Florida State University Tallahassee, FL 3306 {xyuan,nienaber,duan}@cs.fsu.edu
More informationSage Estimating. (formerly Sage Timberline Estimating) Getting Started Guide
Sage Estimating (formerly Sage Timberline Estimating) Getting Started Guide This is a ublication of Sage Software, Inc. Document Number 20001S14030111ER 09/2012 2012 Sage Software, Inc. All rights reserved.
More informationThe Boundary Between Privacy and Utility in Data Publishing
The Boundary Between Privacy and Utility in Data Publishing Vibhor Rastogi Dan Suciu Sungho Hong ABSTRACT We consider the privacy proble in data publishing: given a database instance containing sensitive
More informationOn Performance Bottleneck of Anonymous Communication Networks
On Perforance Bottleneck of Anonyous Counication Networks Ryan Pries, Wei Yu, Steve Graha, and Xinwen Fu Abstract Although a significant aount of effort has been directed at discovering attacks against
More informationSuper-Resolution on Moving Objects using a Polygon-Based Object Description
Super-Resolution on Moving Objects using a Polgon-Based Object Description A. van Eekeren K. Schutte O.R. Oudegeest L.J. van Vliet TNO Defence, Securit and Safet, P.O. Box 96864, 2509 JG, The Hague, The
More informationAPPLICATION NOTE #175
APPLICATION NOTE #175 INTRODUCTION Application Note #175 suppleents the recoended protection circuitry discussions currently found in Ceretek product data sheets. The contents of Application Note #175
More informationHandling Indivisibilities. Notes for AGEC 622. Bruce McCarl Regents Professor of Agricultural Economics Texas A&M University
Notes for AGEC 6 Bruce McCarl Regents Professor of Agricultural Econoics Texas A&M University All or None -- Integer Prograing McCarl and Spreen Chapter 5 Many investent and probles involve cases where
More informationIntegrating fast mobility in the OLSR routing protocol
Integrating fast obility in the OLSR routing protocol Mounir BENZAID 1,2, Pascale MINET 1 and Khaldoun AL AGHA 1,2 1 INRIA, Doaine de Voluceau - B.P.105, 78153 Le Chesnay Cedex, FRANCE ounir.benzaid, pascale.inet@inria.fr
More informationA Beam Search Method to Solve the Problem of Assignment Cells to Switches in a Cellular Mobile Network
A Bea Search Method to Solve the Proble of Assignent Cells to Switches in a Cellular Mobile Networ Cassilda Maria Ribeiro Faculdade de Engenharia de Guaratinguetá - DMA UNESP - São Paulo State University
More informationGeo-activity Recommendations by using Improved Feature Combination
Geo-activity Recoendations by using Iproved Feature Cobination Masoud Sattari Middle East Technical University Ankara, Turkey e76326@ceng.etu.edu.tr Murat Manguoglu Middle East Technical University Ankara,
More informationS3 / S4 Credit Course Overview. original size. Unit 3.4 Money Saving & Spending. Unit 3.1 Calculations. Power key y x. Unit 3.
Unit 3. Calculations Rounding Decial places Significant figures Using the calculator Brackets ( ) Square key x Square root key Power key y x Standard For Write large and sall nuers in standard for Change
More informationScheduling Parallel Real-Time Recurrent Tasks on Multicore Platforms
IEEE TRANSACTIONS ON PARALLEL AND DISTRIBUTED SYSTEMS, VOL., NO., NOV 27 Scheduling Parallel Real-Tie Recurrent Tasks on Multicore Platfors Risat Pathan, Petros Voudouris, and Per Stenströ Abstract We
More informationData pre-processing framework in SPM. Bogdan Draganski
Data pre-processing fraework in SPM Bogdan Draganski Outline Why do we need pre-processing? Overview Structural MRI pre-processing fmri pre-processing Why do we need pre-processing? What do we want? Reason
More informationDistributed Multicast Tree Construction in Wireless Sensor Networks
Distributed Multicast Tree Construction in Wireless Sensor Networks Hongyu Gong, Luoyi Fu, Xinzhe Fu, Lutian Zhao 3, Kainan Wang, and Xinbing Wang Dept. of Electronic Engineering, Shanghai Jiao Tong University,
More information6.1 Topological relations between two simple geometric objects
Chapter 5 proposed a spatial odel to represent the spatial extent of objects in urban areas. The purpose of the odel, as was clarified in Chapter 3, is ultifunctional, i.e. it has to be capable of supplying
More informationAbstract. for L1VPNs [2, 3] and the standardization work at the Internet Engineering Task Force (IETF) is in progress [4].
Traffic Deand Modeling for Dynaic Layer 1 VPN Serices * Joachi Scharf, Martin Köhn Uniersity of Stuttgart, Institute of Counication Networks and Coputer Engineering (IKR), Pfaffenwaldring 47, 70569 Stuttgart,
More informationUtility-Based Resource Allocation for Mixed Traffic in Wireless Networks
IEEE IFOCO 2 International Workshop on Future edia etworks and IP-based TV Utility-Based Resource Allocation for ixed Traffic in Wireless etworks Li Chen, Bin Wang, Xiaohang Chen, Xin Zhang, and Dacheng
More informationInteroperability/ Conformance Test dpmr Mode 2 Repeater
Interoperability/ Conforance Test dpmr Mode 2 Repeater IOP test Mode 2 Repeater Copyright 2013 dpmr Association All Rights Reserved Version 1.0 0 Revision History Version Date Change By 0v1 16 Oct 2012
More informationSage Estimating (SQL) (formerly Sage Timberline Estimating) Installation and Administration Guide. Version 16.11
Sage Estimating (SQL) (formerly Sage Timberline Estimating) Installation and Administration Guide Version 16.11 This is a ublication of Sage Software, Inc. 2016 The Sage Grou lc or its licensors. All rights
More informationDiffraction Review. Two-slit and one-slit patterns. Double-slit diffraction. Single-slit Intensity. Scaling of diffraction patterns
Diffraction Review Today Single-slit diffraction review Multiple slit diffraction review Diffraction intensities Diffraction grating and spectroscopy Suary of single-slit diffraction Given light of wavelength
More informationReal Time Displacement Measurement of an image in a 2D Plane
International Journal of Scientific Research Engineering & Technology (IJSRET), ISSN 2278 0882 Volue 5, Issue 3, March 2016 176 Real Tie Displaceent Measureent of an iage in a 2D Plane Abstract Prashant
More information