Differential Encoding
|
|
- Candace McDowell
- 6 years ago
- Views:
Transcription
1 Dfferetal Ecog C.M. Lu Perceptual Sgal Processg Lab College of Computer Scece Natoal Chao-Tug Uversty Offce: EC538 (03)
2 Iea eucg the yamc rage/varace of coe sequece by ecog sample ffereces
3 Eample Image Hstogram 3 99% [-3, 3] 5 bts/pel (or less)
4 Basc Algorthm 4 Coser sequece: Dffereces: Lossless ecog Smply cog the fferece s suffcet to recover orgal Lossy ecog Quatzer: Quatze sequece: Lossless recostructo: QE: Observato: QE seems to grow over tme s t a cocece?
5 Basc Algorthm () 5 Coser: { } a - q Q ] [ 0 0, 0 ] [ q Q q q ] [ q Q q q q q k k q
6 Basc Algorthm (3) 6 Alteratve cog: 0 ] [ q Q q q ] [ q Q q q q
7 7 Basc Algorthm: Eample
8 8 Dfferetal Ecog Scheme
9 Dfferetal Pulse Coe Moulato (DPCM) 9 p f (,, K, ) 0
10 Precto DPCM 0 σ [( p ) ] E Choce of f ( ) affects σ, however q Depeeces: f ( ) σ f ( ) where q epes o the varace of Fe Quatzato Assumpto: Graularty s fe eough so that Thus, p f (,, K ), 0
11 Lear Prector N s caller orer of the prector N a p ( ) N a E σ F {a }: mmze ( ) [ ] ( ) [ ] 0 0 N N N N a E a a E a σ σ M
12 Lear Prector () N N N a a a ( ( ( ) M ) N ) () () ( N ) where s the autocorrelato fucto: ( k) E[ ] k
13 Lear Prector (3) 3 (0) ) ( ) ( ) ( (0) () ) ( () (0) N N N N L M O M M L L ) ( ) ( ) ( () k k N P a a A M M P. A P A
14 4 Lear Prector Eample: Speech
15 Lear Prector Eample () 5 M k ( ) M k k N a 0.66 N a 0.596, a N 3 a 0.577, a -0.05, a k
16 Lear Prector Eample: Laplaca Quatzato 6 Uform Step szes 4-level: st orer: 0.75, orer: 0.59, 3 r orer: level: st orer: 0.3, orer: 0.4, 3 r orer: 0.5 SN(B) SPE(B) Precto Error M ( ) M M M ( p )
17 Lear Prector Eample: Performace 7 SN creases a lot for orer to orer.
18 Lear Prector Eample: ecostructo 8 Although the recostructe sequece looks lke the orgal, otce that there s sgfcat storto areas where the source output values are small. Orgal ecostructo: 8-level quatzer Laplaca pf 3 r -orer prector
19 Aaptve DPCM 9 Motvato Eve after DPCM, a lot of structure remas the sgal Structure more compresso s possble esuals for 3 r -orer prector
20 Approaches Aaptve DPCM 0 Aaptato ca be apple to Quatzato Precto Observato Quatzato aaptato s epeet of precto Precto aaptato quatzato aaptato Goo precto epes o goo quatzato
21 Aaptve Quatzato DPCM Forwar aaptato Parameters are estmate for each block Trasmtte to recever Overall, ths s coveet DPCM as parameters are ot eplctly avalable (ue to feeback loop) Backwar aaptato Essetally, a verso of the Jayat quatzer Eample: 8-level quatzer, 3 r -orer prector M 0 0.9, M 0.9, M.5, M 3.75
22 Eample: Aaptve Jayat DPCM Orgal Jayat No-aaptve
23 Forwar Aaptve Precto: DPCM-APF 3 Speech cog 8000 sample/sec, 8 samples/block (6ms) Image cog 88 blocks Autocorrelato coeffcets Assumg samples are zero outse block. l meas the l th block. ( l ) ( l ) ( l ) ( k) ( k) ( k) M k M k ( l ) ( k) lm k ( l) M lm ( l) M k, for k > 0 k (k) ca be effcetly ecoe usg partal correlato (parcor) for k < 0 coeffcets
24 Backwar Aaptve Precto: DPCM-APB 4 st -orer prector Aapts wth sample eplacg by to have the cosstet result wth ecoer ) ( ) ( ) ( a a a a α α N th -orer prector j j j j a a X A A ) ( ) ( ) ( ) ( α α A.k.a. Least Mea Square (LMS) ) ( ) ( a a α
25 Delta Moulato (DM) 5 DM DPCM w/ -bt quatzer Samplg frequecy At least twce the hghest frequecy sgal compoet Usually, much hgher
26 6 Lear DM ecostructo
27 Costat Factor Aaptve DM (CFDM) 7 < > 0 f 0 f s Δ Δ Δ f f s s M s s M < < M M M
28 Seco-Orer CFDM 8 Eamples for samples precto
29 Speech Cog 9 Autocorrelato fucto for speech sample Icates a pero of 47 samples Ptch pero Nee a separate compoet to take avatage of t
30 DPCM wth Ptch Prector 30 P : b, τ p τ ptch pero Also: Nose Feeback Cog (NFC) Shapg of QE such that most falls hgh-ampltue peros
31 DPCM wth Ptch Prector Performace 3 DPCM esuals DPCM w/ Ptch Prector esuals
32 G.76 3 ITU recommeato for staar ADPCM Supersees G.7 & G.73 ates: 40/3/4/6 kbts/sec Compresso w.r.t. 8-bt PCM:.6:, :,.67:, 4: Quatzer levels: b - mtrea quatzer Backwar aaptve quatzato A verso of the Jayat quatzer Descrbe terms of a scale factor α k Q[ k /α k ] * α k
33 33 G.76 4kb Quatzer I/O Map
34 G.76: Quatzer Aaptato 34 Base o y(k) log α k Two factors: y u ulocke to hale large fluctuatos (e.g. speech) y l locke for small oes lke ata trasmsso. ( a ( k) ) y ( ) y( k) a ( k) yu( k ) l k a epe o put varace: for speech t s close to y u ( k) 5 [ I ], where W [] log M [], ( ε ) y( k ) εw ε k y l ( k) ( γ ) y ( k ) γ y ( k), γ l u 6
35 G.76: Prector 35 Backwar aaptable base o last recostructe values last 6 quatze ffereces p k ( 6 k) ( k) a k b k Smplfe LMS:
36 Dfferetal Image Cog 36 Coser the prector combato wth -bt uform quatzer & AC bt/pel ecog compare to JPEG at the same rate Dff coe: SNB, PSN3B JPEG: SN33B, PSN4B
37 Dfferetal Image Cog () 37 Improve scheme ecursvely ee quatzer Improve prector Dff coe: SN9B, PSN38B JPEG: SN33B, PSN4B
38 emarks 38 Precto DPCM Aaptve DPCM Delta Moulato Speech Cog Image Cog
39 Homeworks 39 P. 35 3, 4, 6
Dimensionality reduction Feature selection
CS 675 Itroucto to ache Learg Lecture Dmesoalty reucto Feature selecto los Hauskrecht mlos@cs.ptt.eu 539 Seott Square Dmesoalty reucto. otvato. L methos are sestve to the mesoalty of ata Questo: Is there
More informationRecall MLR 5 Homskedasticity error u has the same variance given any values of the explanatory variables Var(u x1,...,xk) = 2 or E(UU ) = 2 I
Chapter 8 Heterosedastcty Recall MLR 5 Homsedastcty error u has the same varace gve ay values of the eplaatory varables Varu,..., = or EUU = I Suppose other GM assumptos hold but have heterosedastcty.
More informationObjectives of Multiple Regression
Obectves of Multple Regresso Establsh the lear equato that best predcts values of a depedet varable Y usg more tha oe eplaator varable from a large set of potetal predctors {,,... k }. Fd that subset of
More information( ) = ( ) ( ) Chapter 13 Asymptotic Theory and Stochastic Regressors. Stochastic regressors model
Chapter 3 Asmptotc Theor ad Stochastc Regressors The ature of eplaator varable s assumed to be o-stochastc or fed repeated samples a regresso aalss Such a assumpto s approprate for those epermets whch
More informationSTATISTICAL PROPERTIES OF LEAST SQUARES ESTIMATORS. x, where. = y - ˆ " 1
STATISTICAL PROPERTIES OF LEAST SQUARES ESTIMATORS Recall Assumpto E(Y x) η 0 + η x (lear codtoal mea fucto) Data (x, y ), (x 2, y 2 ),, (x, y ) Least squares estmator ˆ E (Y x) ˆ " 0 + ˆ " x, where ˆ
More information= 2. Statistic - function that doesn't depend on any of the known parameters; examples:
of Samplg Theory amples - uemploymet househol cosumpto survey Raom sample - set of rv's... ; 's have ot strbuto [ ] f f s vector of parameters e.g. Statstc - fucto that oes't epe o ay of the ow parameters;
More informationENGI 3423 Simple Linear Regression Page 12-01
ENGI 343 mple Lear Regresso Page - mple Lear Regresso ometmes a expermet s set up where the expermeter has cotrol over the values of oe or more varables X ad measures the resultg values of aother varable
More informationEconometric Methods. Review of Estimation
Ecoometrc Methods Revew of Estmato Estmatg the populato mea Radom samplg Pot ad terval estmators Lear estmators Ubased estmators Lear Ubased Estmators (LUEs) Effcecy (mmum varace) ad Best Lear Ubased Estmators
More informationLecture 7. Confidence Intervals and Hypothesis Tests in the Simple CLR Model
Lecture 7. Cofdece Itervals ad Hypothess Tests the Smple CLR Model I lecture 6 we troduced the Classcal Lear Regresso (CLR) model that s the radom expermet of whch the data Y,,, K, are the outcomes. The
More informationSummary of the lecture in Biostatistics
Summary of the lecture Bostatstcs Probablty Desty Fucto For a cotuos radom varable, a probablty desty fucto s a fucto such that: 0 dx a b) b a dx A probablty desty fucto provdes a smple descrpto of the
More informationLinear Regression with One Regressor
Lear Regresso wth Oe Regressor AIM QA.7. Expla how regresso aalyss ecoometrcs measures the relatoshp betwee depedet ad depedet varables. A regresso aalyss has the goal of measurg how chages oe varable,
More informationChapter 13 Student Lecture Notes 13-1
Chapter 3 Studet Lecture Notes 3- Basc Busess Statstcs (9 th Edto) Chapter 3 Smple Lear Regresso 4 Pretce-Hall, Ic. Chap 3- Chapter Topcs Types of Regresso Models Determg the Smple Lear Regresso Equato
More informationDecode. Encode. Page 6. Speech Coding E.4.14 Speech Processing. Speech Coding. Lecture 8. Speech Coding. s(n)
Page 6. Speech Codg E.4.4 Speech Processg CODE.PPT(4/5/) 6. CODE.PPT(4/5/) 6. Lecture 8 Speech Codg Speech Codg Objectves of Speech Codg Qualty versus bt rate Uform Quatzato Quatsato Nose No-uform Quatsato
More informationECONOMETRIC THEORY. MODULE VIII Lecture - 26 Heteroskedasticity
ECONOMETRIC THEORY MODULE VIII Lecture - 6 Heteroskedastcty Dr. Shalabh Departmet of Mathematcs ad Statstcs Ida Isttute of Techology Kapur . Breusch Paga test Ths test ca be appled whe the replcated data
More informationSource-Channel Prediction in Error Resilient Video Coding
Source-Chael Predcto Error Reslet Vdeo Codg Hua Yag ad Keeth Rose Sgal Compresso Laboratory ECE Departmet Uversty of Calfora, Sata Barbara Outle Itroducto Source-chael predcto Smulato results Coclusos
More informationStatistics MINITAB - Lab 5
Statstcs 10010 MINITAB - Lab 5 PART I: The Correlato Coeffcet Qute ofte statstcs we are preseted wth data that suggests that a lear relatoshp exsts betwee two varables. For example the plot below s of
More informationKernel-based Methods and Support Vector Machines
Kerel-based Methods ad Support Vector Maches Larr Holder CptS 570 Mache Learg School of Electrcal Egeerg ad Computer Scece Washgto State Uverst Refereces Muller et al. A Itroducto to Kerel-Based Learg
More informationUNIT 7 RANK CORRELATION
UNIT 7 RANK CORRELATION Rak Correlato Structure 7. Itroucto Objectves 7. Cocept of Rak Correlato 7.3 Dervato of Rak Correlato Coeffcet Formula 7.4 Te or Repeate Raks 7.5 Cocurret Devato 7.6 Summar 7.7
More informationENGI 4421 Propagation of Error Page 8-01
ENGI 441 Propagato of Error Page 8-01 Propagato of Error [Navd Chapter 3; ot Devore] Ay realstc measuremet procedure cotas error. Ay calculatos based o that measuremet wll therefore also cota a error.
More informationMultiple Choice Test. Chapter Adequacy of Models for Regression
Multple Choce Test Chapter 06.0 Adequac of Models for Regresso. For a lear regresso model to be cosdered adequate, the percetage of scaled resduals that eed to be the rage [-,] s greater tha or equal to
More informationSpecial Instructions / Useful Data
JAM 6 Set of all real umbers P A..d. B, p Posso Specal Istructos / Useful Data x,, :,,, x x Probablty of a evet A Idepedetly ad detcally dstrbuted Bomal dstrbuto wth parameters ad p Posso dstrbuto wth
More informationUNIVERSITY OF OSLO DEPARTMENT OF ECONOMICS
UNIVERSITY OF OSLO DEPARTMENT OF ECONOMICS Postpoed exam: ECON430 Statstcs Date of exam: Jauary 0, 0 Tme for exam: 09:00 a.m. :00 oo The problem set covers 5 pages Resources allowed: All wrtte ad prted
More informationComparison of Dual to Ratio-Cum-Product Estimators of Population Mean
Research Joural of Mathematcal ad Statstcal Sceces ISS 30 6047 Vol. 1(), 5-1, ovember (013) Res. J. Mathematcal ad Statstcal Sc. Comparso of Dual to Rato-Cum-Product Estmators of Populato Mea Abstract
More informationLinear Regression Linear Regression with Shrinkage. Some slides are due to Tommi Jaakkola, MIT AI Lab
Lear Regresso Lear Regresso th Shrkage Some sldes are due to Tomm Jaakkola, MIT AI Lab Itroducto The goal of regresso s to make quattatve real valued predctos o the bass of a vector of features or attrbutes.
More informationAnalysis of Variance with Weibull Data
Aalyss of Varace wth Webull Data Lahaa Watthaacheewaul Abstract I statstcal data aalyss by aalyss of varace, the usual basc assumptos are that the model s addtve ad the errors are radomly, depedetly, ad
More informationD. VQ WITH 1ST-ORDER LOSSLESS CODING
VARIABLE-RATE VQ (AKA VQ WITH ENTROPY CODING) Varable-Rate VQ = Quatzato + Lossless Varable-Legth Bary Codg A rage of optos -- from smple to complex A. Uform scalar quatzato wth varable-legth codg, oe
More informationParameter, Statistic and Random Samples
Parameter, Statstc ad Radom Samples A parameter s a umber that descrbes the populato. It s a fxed umber, but practce we do ot kow ts value. A statstc s a fucto of the sample data,.e., t s a quatty whose
More informationDimensionality reduction Feature selection
CS 750 Mache Learg Lecture 3 Dmesoalty reducto Feature selecto Mlos Hauskrecht mlos@cs.ptt.edu 539 Seott Square CS 750 Mache Learg Dmesoalty reducto. Motvato. Classfcato problem eample: We have a put data
More information{ }{ ( )} (, ) = ( ) ( ) ( ) Chapter 14 Exercises in Sampling Theory. Exercise 1 (Simple random sampling): Solution:
Chapter 4 Exercses Samplg Theory Exercse (Smple radom samplg: Let there be two correlated radom varables X ad A sample of sze s draw from a populato by smple radom samplg wthout replacemet The observed
More information12.2 Estimating Model parameters Assumptions: ox and y are related according to the simple linear regression model
1. Estmatg Model parameters Assumptos: ox ad y are related accordg to the smple lear regresso model (The lear regresso model s the model that says that x ad y are related a lear fasho, but the observed
More informationChapter 8. Inferences about More Than Two Population Central Values
Chapter 8. Ifereces about More Tha Two Populato Cetral Values Case tudy: Effect of Tmg of the Treatmet of Port-We tas wth Lasers ) To vestgate whether treatmet at a youg age would yeld better results tha
More informationLinear regression (cont) Logistic regression
CS 7 Fouatos of Mache Lear Lecture 4 Lear reresso cot Lostc reresso Mlos Hausrecht mlos@cs.ptt.eu 539 Seott Square Lear reresso Vector efto of the moel Iclue bas costat the put vector f - parameters ehts
More information3. Basic Concepts: Consequences and Properties
: 3. Basc Cocepts: Cosequeces ad Propertes Markku Jutt Overvew More advaced cosequeces ad propertes of the basc cocepts troduced the prevous lecture are derved. Source The materal s maly based o Sectos.6.8
More informationAnalysis of System Performance IN2072 Chapter 5 Analysis of Non Markov Systems
Char for Network Archtectures ad Servces Prof. Carle Departmet of Computer Scece U Müche Aalyss of System Performace IN2072 Chapter 5 Aalyss of No Markov Systems Dr. Alexader Kle Prof. Dr.-Ig. Georg Carle
More informationf X (x i ;θ) = n ( n logx i = 0 = θml = n/ n logx i 1 θ +1 n n 2 < 0 for all θ (θ +1) n logx i 1 ESTIMATOR: = logx i θ n for all θ θ 2 < 0 2θ 2 x 3
MATH 557 - EXERCISES SOLUTIONS 1 The lkelhoo the orgal parameterzato s ( ( 1 L (θ 1,θ x 1,x = θ x 1 1 x (1 θ 1 1 x 1 θ x 1 x (1 θ x If φ = θ /(1 θ, the θ = φ/(1+φ θ 1 = (φψ/(1+φψ. Ether by wrtg out the
More information(Monte Carlo) Resampling Technique in Validity Testing and Reliability Testing
Iteratoal Joural of Computer Applcatos (0975 8887) (Mote Carlo) Resamplg Techque Valdty Testg ad Relablty Testg Ad Setawa Departmet of Mathematcs, Faculty of Scece ad Mathematcs, Satya Wacaa Chrsta Uversty
More informationSupervised learning: Linear regression Logistic regression
CS 57 Itroducto to AI Lecture 4 Supervsed learg: Lear regresso Logstc regresso Mlos Hauskrecht mlos@cs.ptt.edu 539 Seott Square CS 57 Itro to AI Data: D { D D.. D D Supervsed learg d a set of eamples s
More information9 U-STATISTICS. Eh =(m!) 1 Eh(X (1),..., X (m ) ) i.i.d
9 U-STATISTICS Suppose,,..., are P P..d. wth CDF F. Our goal s to estmate the expectato t (P)=Eh(,,..., m ). Note that ths expectato requres more tha oe cotrast to E, E, or Eh( ). Oe example s E or P((,
More informationLecture 3 Probability review (cont d)
STATS 00: Itroducto to Statstcal Iferece Autum 06 Lecture 3 Probablty revew (cot d) 3. Jot dstrbutos If radom varables X,..., X k are depedet, the ther dstrbuto may be specfed by specfyg the dvdual dstrbuto
More informationMultiple Regression. More than 2 variables! Grade on Final. Multiple Regression 11/21/2012. Exam 2 Grades. Exam 2 Re-grades
STAT 101 Dr. Kar Lock Morga 11/20/12 Exam 2 Grades Multple Regresso SECTIONS 9.2, 10.1, 10.2 Multple explaatory varables (10.1) Parttog varablty R 2, ANOVA (9.2) Codtos resdual plot (10.2) Trasformatos
More informationCS 2750 Machine Learning. Lecture 8. Linear regression. CS 2750 Machine Learning. Linear regression. is a linear combination of input components x
CS 75 Mache Learg Lecture 8 Lear regresso Mlos Hauskrecht mlos@cs.ptt.edu 539 Seott Square CS 75 Mache Learg Lear regresso Fucto f : X Y s a lear combato of put compoets f + + + K d d K k - parameters
More informationScalar Quantization for Audio Data Coding
Scalar Quatzato for Audo Data Codg Bors D. Kudryashov, Ato V. Porov, ad Eum L. Oh Abstract Ths paper s cocered wth scalar quatzato of trasform coeffcets a audo codec. The geeralzed Gaussa dstrbuto (GG
More informationENGI 4421 Joint Probability Distributions Page Joint Probability Distributions [Navidi sections 2.5 and 2.6; Devore sections
ENGI 441 Jot Probablty Dstrbutos Page 7-01 Jot Probablty Dstrbutos [Navd sectos.5 ad.6; Devore sectos 5.1-5.] The jot probablty mass fucto of two dscrete radom quattes, s, P ad p x y x y The margal probablty
More informationLine Fitting and Regression
Marquette Uverst MSCS6 Le Fttg ad Regresso Dael B. Rowe, Ph.D. Professor Departmet of Mathematcs, Statstcs, ad Computer Scece Coprght 8 b Marquette Uverst Least Squares Regresso MSCS6 For LSR we have pots
More informationSampling Theory MODULE V LECTURE - 14 RATIO AND PRODUCT METHODS OF ESTIMATION
Samplg Theor MODULE V LECTUE - 4 ATIO AND PODUCT METHODS OF ESTIMATION D. SHALABH DEPATMENT OF MATHEMATICS AND STATISTICS INDIAN INSTITUTE OF TECHNOLOG KANPU A mportat objectve a statstcal estmato procedure
More informationi 2 σ ) i = 1,2,...,n , and = 3.01 = 4.01
ECO 745, Homework 6 Le Cabrera. Assume that the followg data come from the lear model: ε ε ~ N, σ,,..., -6. -.5 7. 6.9 -. -. -.9. -..6.4.. -.6 -.7.7 Fd the mamum lkelhood estmates of,, ad σ ε s.6. 4. ε
More informationChapter 5 Properties of a Random Sample
Lecture 6 o BST 63: Statstcal Theory I Ku Zhag, /0/008 Revew for the prevous lecture Cocepts: t-dstrbuto, F-dstrbuto Theorems: Dstrbutos of sample mea ad sample varace, relatoshp betwee sample mea ad sample
More informationMean is only appropriate for interval or ratio scales, not ordinal or nominal.
Mea Same as ordary average Sum all the data values ad dvde by the sample sze. x = ( x + x +... + x Usg summato otato, we wrte ths as x = x = x = = ) x Mea s oly approprate for terval or rato scales, ot
More informationThe expected value of a sum of random variables,, is the sum of the expected values:
Sums of Radom Varables xpected Values ad Varaces of Sums ad Averages of Radom Varables The expected value of a sum of radom varables, say S, s the sum of the expected values: ( ) ( ) S Ths s always true
More informationOrdinary Least Squares Regression. Simple Regression. Algebra and Assumptions.
Ordary Least Squares egresso. Smple egresso. Algebra ad Assumptos. I ths part of the course we are gog to study a techque for aalysg the lear relatoshp betwee two varables Y ad X. We have pars of observatos
More informationCODING & MODULATION Prof. Ing. Anton Čižmár, PhD.
CODING & MODULATION Prof. Ig. Ato Čžmár, PhD. also from Dgtal Commucatos 4th Ed., J. G. Proaks, McGraw-Hll It. Ed. 00 CONTENT. PROBABILITY. STOCHASTIC PROCESSES Probablty ad Stochastc Processes The theory
More informationUNIVERSITY OF OSLO DEPARTMENT OF ECONOMICS
UNIVERSITY OF OSLO DEPARTMENT OF ECONOMICS Exam: ECON430 Statstcs Date of exam: Frday, December 8, 07 Grades are gve: Jauary 4, 08 Tme for exam: 0900 am 00 oo The problem set covers 5 pages Resources allowed:
More informationInvestigation of Partially Conditional RP Model with Response Error. Ed Stanek
Partally Codtoal Radom Permutato Model 7- vestgato of Partally Codtoal RP Model wth Respose Error TRODUCTO Ed Staek We explore the predctor that wll result a smple radom sample wth respose error whe a
More informationMultivariate Transformation of Variables and Maximum Likelihood Estimation
Marquette Uversty Multvarate Trasformato of Varables ad Maxmum Lkelhood Estmato Dael B. Rowe, Ph.D. Assocate Professor Departmet of Mathematcs, Statstcs, ad Computer Scece Copyrght 03 by Marquette Uversty
More informationA New Family of Transformations for Lifetime Data
Proceedgs of the World Cogress o Egeerg 4 Vol I, WCE 4, July - 4, 4, Lodo, U.K. A New Famly of Trasformatos for Lfetme Data Lakhaa Watthaacheewakul Abstract A famly of trasformatos s the oe of several
More informationVARIABLE-RATE VQ (AKA VQ WITH ENTROPY CODING)
VARIABLE-RATE VQ (AKA VQ WITH ENTROPY CODING) Varable-Rate VQ = Quatzato + Lossless Varable-Legth Bary Codg A rage of optos -- from smple to complex a. Uform scalar quatzato wth varable-legth codg, oe
More informationMidterm Exam 1, section 2 (Solution) Thursday, February hour, 15 minutes
coometrcs, CON Sa Fracsco State Uverst Mchael Bar Sprg 5 Mdterm xam, secto Soluto Thursda, Februar 6 hour, 5 mutes Name: Istructos. Ths s closed book, closed otes exam.. No calculators of a kd are allowed..
More informationSignal,autocorrelation -0.6
Sgal,autocorrelato Phase ose p/.9.3.7. -.5 5 5 5 Tme Sgal,autocorrelato Phase ose p/.5..7.3 -. -.5 5 5 5 Tme Sgal,autocorrelato. Phase ose p/.9.3.7. -.5 5 5 5 Tme Sgal,autocorrelato. Phase ose p/.8..6.
More informationSolutions to Odd-Numbered End-of-Chapter Exercises: Chapter 17
Itroucto to Ecoometrcs (3 r Upate Eto) by James H. Stock a Mark W. Watso Solutos to O-Numbere E-of-Chapter Exercses: Chapter 7 (Ths erso August 7, 04) 05 Pearso Eucato, Ic. Stock/Watso - Itroucto to Ecoometrcs
More informationChapter 2 Supplemental Text Material
-. Models for the Data ad the t-test Chapter upplemetal Text Materal The model preseted the text, equato (-3) s more properl called a meas model. ce the mea s a locato parameter, ths tpe of model s also
More information4. Standard Regression Model and Spatial Dependence Tests
4. Stadard Regresso Model ad Spatal Depedece Tests Stadard regresso aalss fals the presece of spatal effects. I case of spatal depedeces ad/or spatal heterogeet a stadard regresso model wll be msspecfed.
More informationModel Fitting, RANSAC. Jana Kosecka
Model Fttg, RANSAC Jaa Kosecka Fttg: Issues Prevous strateges Le detecto Hough trasform Smple parametrc model, two parameters m, b m + b Votg strateg Hard to geeralze to hgher dmesos a o + a + a 2 2 +
More informationPGE 310: Formulation and Solution in Geosystems Engineering. Dr. Balhoff. Interpolation
PGE 30: Formulato ad Soluto Geosystems Egeerg Dr. Balhoff Iterpolato Numercal Methods wth MATLAB, Recktewald, Chapter 0 ad Numercal Methods for Egeers, Chapra ad Caale, 5 th Ed., Part Fve, Chapter 8 ad
More informationMarcinkiewicz strong laws for linear statistics of ρ -mixing sequences of random variables
Aas da Academa Braslera de Cêcas 2006 784: 65-62 Aals of the Brazla Academy of Sceces ISSN 000-3765 www.scelo.br/aabc Marckewcz strog laws for lear statstcs of ρ -mxg sequeces of radom varables GUANG-HUI
More informationhp calculators HP 30S Statistics Averages and Standard Deviations Average and Standard Deviation Practice Finding Averages and Standard Deviations
HP 30S Statstcs Averages ad Stadard Devatos Average ad Stadard Devato Practce Fdg Averages ad Stadard Devatos HP 30S Statstcs Averages ad Stadard Devatos Average ad stadard devato The HP 30S provdes several
More informationPart 4b Asymptotic Results for MRR2 using PRESS. Recall that the PRESS statistic is a special type of cross validation procedure (see Allen (1971))
art 4b Asymptotc Results for MRR usg RESS Recall that the RESS statstc s a specal type of cross valdato procedure (see Alle (97)) partcular to the regresso problem ad volves fdg Y $,, the estmate at the
More informationEvaluation of uncertainty in measurements
Evaluato of ucertaty measuremets Laboratory of Physcs I Faculty of Physcs Warsaw Uversty of Techology Warszawa, 05 Itroducto The am of the measuremet s to determe the measured value. Thus, the measuremet
More informationEconometrics. 3) Statistical properties of the OLS estimator
30C0000 Ecoometrcs 3) Statstcal propertes of the OLS estmator Tmo Kuosmae Professor, Ph.D. http://omepre.et/dex.php/tmokuosmae Today s topcs Whch assumptos are eeded for OLS to work? Statstcal propertes
More informationLikelihood Ratio, Wald, and Lagrange Multiplier (Score) Tests. Soccer Goals in European Premier Leagues
Lkelhood Rato, Wald, ad Lagrage Multpler (Score) Tests Soccer Goals Europea Premer Leagues - 4 Statstcal Testg Prcples Goal: Test a Hpothess cocerg parameter value(s) a larger populato (or ature), based
More informationChapter 11 Systematic Sampling
Chapter stematc amplg The sstematc samplg techue s operatoall more coveet tha the smple radom samplg. It also esures at the same tme that each ut has eual probablt of cluso the sample. I ths method of
More informationChapter 4 (Part 1): Non-Parametric Classification (Sections ) Pattern Classification 4.3) Announcements
Aoucemets No-Parametrc Desty Estmato Techques HW assged Most of ths lecture was o the blacboard. These sldes cover the same materal as preseted DHS Bometrcs CSE 90-a Lecture 7 CSE90a Fall 06 CSE90a Fall
More informationCSE 5526: Introduction to Neural Networks Linear Regression
CSE 556: Itroducto to Neural Netorks Lear Regresso Part II 1 Problem statemet Part II Problem statemet Part II 3 Lear regresso th oe varable Gve a set of N pars of data , appromate d by a lear fucto
More informationSimple Linear Regression
Statstcal Methods I (EST 75) Page 139 Smple Lear Regresso Smple regresso applcatos are used to ft a model descrbg a lear relatoshp betwee two varables. The aspects of least squares regresso ad correlato
More informationChapter 8: Statistical Analysis of Simulated Data
Marquette Uversty MSCS600 Chapter 8: Statstcal Aalyss of Smulated Data Dael B. Rowe, Ph.D. Departmet of Mathematcs, Statstcs, ad Computer Scece Copyrght 08 by Marquette Uversty MSCS600 Ageda 8. The Sample
More informationSTA 108 Applied Linear Models: Regression Analysis Spring Solution for Homework #1
STA 08 Appled Lear Models: Regresso Aalyss Sprg 0 Soluto for Homework #. Let Y the dollar cost per year, X the umber of vsts per year. The the mathematcal relato betwee X ad Y s: Y 300 + X. Ths s a fuctoal
More informationChapter 14 Logistic Regression Models
Chapter 4 Logstc Regresso Models I the lear regresso model X β + ε, there are two types of varables explaatory varables X, X,, X k ad study varable y These varables ca be measured o a cotuous scale as
More informationCS 2750 Machine Learning Lecture 8. Linear regression. Supervised learning. a set of n examples
CS 75 Mache Learg Lecture 8 Lear regresso Mlos Hauskrecht los@cs.tt.eu 59 Seott Square Suervse learg Data: D { D D.. D} a set of eales D s a ut vector of sze s the esre outut gve b a teacher Obectve: lear
More informationIdea is to sample from a different distribution that picks points in important regions of the sample space. Want ( ) ( ) ( ) E f X = f x g x dx
Importace Samplg Used for a umber of purposes: Varace reducto Allows for dffcult dstrbutos to be sampled from. Sestvty aalyss Reusg samples to reduce computatoal burde. Idea s to sample from a dfferet
More informationMEASURES OF DISPERSION
MEASURES OF DISPERSION Measure of Cetral Tedecy: Measures of Cetral Tedecy ad Dsperso ) Mathematcal Average: a) Arthmetc mea (A.M.) b) Geometrc mea (G.M.) c) Harmoc mea (H.M.) ) Averages of Posto: a) Meda
More informationECON 482 / WH Hong The Simple Regression Model 1. Definition of the Simple Regression Model
ECON 48 / WH Hog The Smple Regresso Model. Defto of the Smple Regresso Model Smple Regresso Model Expla varable y terms of varable x y = β + β x+ u y : depedet varable, explaed varable, respose varable,
More informationLECTURE 24 LECTURE OUTLINE
LECTURE 24 LECTURE OUTLINE Gradet proxmal mmzato method Noquadratc proxmal algorthms Etropy mmzato algorthm Expoetal augmeted Lagraga mehod Etropc descet algorthm **************************************
More informationSTRONG CONSISTENCY OF LEAST SQUARES ESTIMATE IN MULTIPLE REGRESSION WHEN THE ERROR VARIANCE IS INFINITE
Statstca Sca 9(1999), 289-296 STRONG CONSISTENCY OF LEAST SQUARES ESTIMATE IN MULTIPLE REGRESSION WHEN THE ERROR VARIANCE IS INFINITE J Mgzhog ad Che Xru GuZhou Natoal College ad Graduate School, Chese
More informationThe TDT. (Transmission Disequilibrium Test) (Qualitative and quantitative traits) D M D 1 M 1 D 2 M 2 M 2D1 M 1
The TDT (Trasmsso Dsequlbrum Test) (Qualtatve ad quattatve trats) Our am s to test for lkage (ad maybe ad/or assocato) betwee a dsease locus D ad a marker locus M. We kow where (.e. o what chromosome,
More informationBias Correction in Estimation of the Population Correlation Coefficient
Kasetsart J. (Nat. Sc.) 47 : 453-459 (3) Bas Correcto Estmato of the opulato Correlato Coeffcet Juthaphor Ssomboothog ABSTRACT A estmator of the populato correlato coeffcet of two varables for a bvarate
More informationGeneralized Linear Regression with Regularization
Geeralze Lear Regresso wth Regularzato Zoya Bylsk March 3, 05 BASIC REGRESSION PROBLEM Note: I the followg otes I wll make explct what s a vector a what s a scalar usg vec t or otato, to avo cofuso betwee
More informationLecture 07: Poles and Zeros
Lecture 07: Poles ad Zeros Defto of poles ad zeros The trasfer fucto provdes a bass for determg mportat system respose characterstcs wthout solvg the complete dfferetal equato. As defed, the trasfer fucto
More informationBootstrap Method for Testing of Equality of Several Coefficients of Variation
Cloud Publcatos Iteratoal Joural of Advaced Mathematcs ad Statstcs Volume, pp. -6, Artcle ID Sc- Research Artcle Ope Access Bootstrap Method for Testg of Equalty of Several Coeffcets of Varato Dr. Navee
More informationLecture 1: Introduction to Regression
Lecture : Itroducto to Regresso A Eample: Eplag State Homcde Rates What kds of varables mght we use to epla/predct state homcde rates? Let s cosder just oe predctor for ow: povert Igore omtted varables,
More informationSimulation Output Analysis
Smulato Output Aalyss Summary Examples Parameter Estmato Sample Mea ad Varace Pot ad Iterval Estmato ermatg ad o-ermatg Smulato Mea Square Errors Example: Sgle Server Queueg System x(t) S 4 S 4 S 3 S 5
More informationSTA302/1001-Fall 2008 Midterm Test October 21, 2008
STA3/-Fall 8 Mdterm Test October, 8 Last Name: Frst Name: Studet Number: Erolled (Crcle oe) STA3 STA INSTRUCTIONS Tme allowed: hour 45 mutes Ads allowed: A o-programmable calculator A table of values from
More informationε. Therefore, the estimate
Suggested Aswers, Problem Set 3 ECON 333 Da Hugerma. Ths s ot a very good dea. We kow from the secod FOC problem b) that ( ) SSE / = y x x = ( ) Whch ca be reduced to read y x x = ε x = ( ) The OLS model
More informationBasics of Information Theory: Markku Juntti. Basic concepts and tools 1 Introduction 2 Entropy, relative entropy and mutual information
: Markku Jutt Overvew Te basc cocepts o ormato teory lke etropy mutual ormato ad EP are eeralzed or cotuous-valued radom varables by troduc deretal etropy ource Te materal s maly based o Capter 9 o te
More informationUniform DFT Filter Banks 1/27
.. Ufor FT Flter Baks /27 Ufor FT Flter Baks We ll look at 5 versos of FT-based flter baks all but the last two have serous ltatos ad are t practcal. But they gve a ce trasto to the last two versos whch
More informationSTA 105-M BASIC STATISTICS (This is a multiple choice paper.)
DCDM BUSINESS SCHOOL September Mock Eamatos STA 0-M BASIC STATISTICS (Ths s a multple choce paper.) Tme: hours 0 mutes INSTRUCTIONS TO CANDIDATES Do ot ope ths questo paper utl you have bee told to do
More information1 Solution to Problem 6.40
1 Soluto to Problem 6.40 (a We wll wrte T τ (X 1,...,X where the X s are..d. wth PDF f(x µ, σ 1 ( x µ σ g, σ where the locato parameter µ s ay real umber ad the scale parameter σ s > 0. Lettg Z X µ σ we
More informationEP2200 Queueing theory and teletraffic systems. Queueing networks. Viktoria Fodor KTH EES/LCN KTH EES/LCN
EP2200 Queueg theory ad teletraffc systems Queueg etworks Vktora Fodor Ope ad closed queug etworks Queug etwork: etwork of queug systems E.g. data packets traversg the etwork from router to router Ope
More informationTEMPORAL SCALING OF HYDROLOGICAL AND CLIMATE TIME SERIES AND THE LOW FREQUENCY VARIABILITY
TEMPORAL SCALING OF HYDROLOGICAL AND CLIMATE TIME SERIES AND THE LOW FREQUENCY VARIABILITY Dajela Markovć, Mafred Koch ad Holger Lage 3 Lebz Uversty of Haover Isttute of Meteorology ad Clmatology Uversty
More informationSPECTRAL ANALYSIS OF A SIGNAL DRIVEN SAMPLING SCHEME
4th Europea Sgal Processg Coferece (EUSIPCO 006), Florece, Italy, September 4-8, 006, copyrght by EURASIP SPECTRAL AALYSIS OF A SIGAL DRIVE SAMPLIG SCHEME Saeed Ma Qasar, Lauret Fesquet, Marc Reaud TIMA,
More informationDeterministic Constant Demand Models
Determstc Costat Demad Models George Lberopoulos Ecoomc Order uatty (EO): basc model 3 4 vetory λ λ Parts to customers wth costat rate λ λ λ EO: basc model Assumptos/otato Costat demad rate: λ (parts per
More informationMaximum Likelihood Estimation
Marquette Uverst Maxmum Lkelhood Estmato Dael B. Rowe, Ph.D. Professor Departmet of Mathematcs, Statstcs, ad Computer Scece Coprght 08 b Marquette Uverst Maxmum Lkelhood Estmato We have bee sag that ~
More information