Consider unordered sample of size r. This sample can be used to make r! Ordered samples (r! permutations). unordered sample

Size: px
Start display at page:

Download "Consider unordered sample of size r. This sample can be used to make r! Ordered samples (r! permutations). unordered sample"

Transcription

1 Uodeed Samples wthout Replacemet osde populato of elemets a,,..., a a. y uodeed aagemet of elemets s called a uodeed sample of sze. Two uodeed samples ae dffeet oly f oe cotas a elemet ot cotaed the othe. osde uodeed sample of sze. Ths sample ca be used to make Odeed samples ( pemutatos). ombatos uodeed sample: (a,b,c) emutato odeed samples: (a,b,c); (a,c,b); (b,a,c) (b,c,a); (c,a,b); (c,b,a) The total umbe of odeed samples ( ) 6 Theefoe, Numbe of odeed samples Numbe of uodeed samples Numbe of odeed samples pe uodeed sample ( ) ( )...( ) * *... * ( ) ( ) box cotas 75 good I chps ad 5 defectve chps, ad chps ae selected at adom. Let: at least oe chp s defectve Fd () S 00 o chp of sample s defectve 75

2 ( ) Hece, S ( ) ( ) dge 635,03,559, 600 dffeet bdge hads. 3 5 oke,598, 960 dffeet poke hads. 5 Fo example, Let had of poke cotas fve dffeet face values These face values ca be chose to choose oe of fou suts. Thus, o each cad: 3 ways, ad coespodg to each cad we ae fee S 5 ( ) osde dstgushable balls cells. Defe, specfed cell cotas exactly K balls k a place balls cells dffeet ways. S. K balls ca be chose dffeet k ways. The emag (-k) balls ca be placed to the emag - cells ( ) k ways. k ( ) k ( k ) How may les coect 5 pots wth o 3 co-lea. To aswe ths we must select pots (whch defe a le) fom the 5 gve pots,.e.

3 3 5 Multomal oeffcets Now, cosde the followg stuato: set of dstct tems s to be dvded to dstct goups of espectve szes,,, whee. How may dffeet dvsos ae possble? To aswe ths, we ote that thee ae possble choces fo the fst goup; fo each choce of the fst goup thee ae possble choces fo the secod goup; fo each choce of the fst two goups thee ae possble choces fo the thd goup; ad so o. Hece t follows fom the geealzed 3 veso of the basc coutg pcple that thee ae So we ca state: If F HG IF KJ HG ( 3 ) ( ) ( ) 0 possble dvsos. I KJ F HG,,, we defe by,, I KJ Thus,,, epesets the umbe of possble dvsos o dstct objects to dstct goups of espectve szes, polce depatmet a small cty cossts of 0 offces. If the depatmet polcy s to have 5 of the offces patollg the steets, of the offces wokg full tme at the stato, ad 3 of the offces o eseve at the stato, how may dffeet dvsos of the 0b offces to 3 goups ae possble?

4 4 Soluto: Thee ae dvsos. Thee ae 0 boys who ae to be dvded to a team ad a team of 5 boys each. The team wll play oe league ad the team aothe. How may dffeet dvsos ae possble? Soluto: thee ae 0 55 possble dvsos. I ode to play a game of basketball, 0 boys at a playgoud dvde themselves to two teams of 5 each. How may dffeet dvsos ae possble? Soluto: Note that ths example s dffeet fom the pevous oe because ow the ode of the two of the two teams s elevat. That s, thee s o ad teams but just a dvso cosstg of goups of 5 boys each. That s, thee ae twce () as may dvsos the fst case as ths case. If thee wee thee teams, the thee would be 3 s may dvsos tha f thee wee teams, ad. Hece the desed aswe s 0/ 55 6 Summay: Ths s a good place to summaze. emutato: ombato: Defto: The umbe of dstct aagemets that ca be made fom the elemets of S, usg of them at a tme, s deoted by ( ) ad called the umbe of pemutatos of thgs take at a tme. Note that. Ode s mpotat. Defto: The umbe of dstct subsets of sze that ca be fomed fom the elemets f S s deoted by, ad s called umbe of combatos of thgs take at a tme. Note that. Ode s ot mpotat, (bomal coeffcet) -Whe the sample cotas seveal sets of detcal elemets, we have pemutato wth epetto, o udstgushable sets. The umbe of pemutatos of objects of whch ae alke, ae alke,... ae alke, etc Hece ( ),..., mult-omal... If we have objects, - pemutatos exst. If ad ae alke the # of dstgushable pemutatos.

5 5 How may dffeet sgals, each cosstg of 8 flags hug a vetcal le, ca be fomed fom a set of 4 dstgushable ed flags, thee dstgushable whte flags, ad a blue flag? We seek the umbe of pemutatos of 8 objects of whch 4 ae alke (the ed flags) ad 3 ae alke (the whte flags). y the above theoem, thee ae, 8 8* 7 * 6 *5* 4 * 3* * 80 dffeet sgals * 3* ** 3* * How may ways ca people be dvded to 3 ows of 4 each? (Ode s ot mpotat) ,4, te-chagg ows oce selected Two sets of tmes ae cluded a goup of eght people. How may ways ca sx dstgushable people fom ths goup be aaged a ow? takg cae of pemutatos the ow dace class cossts of studets, 0 wome qd me. f 5 me ad 5 wome ae to be chose ad the paed off, how may esults ae possble? 0 Soluto: Thee ae 5 5 possble choces of the 5 me ad 5 wome. They ca the be paed up 5 Ways, suce f we abtaly ode the e the the fst ma ca be paed wth ay of the 5 wome. Thewxt wth ay of the emag 4, ad so o. Hece 0 thee ae possble esults. I howmay ways ca detcal balls be dstbuted to us so that the th u cotas at least m balls, fo each,,? ssume that Soluto: The umbe of tege solutos of x x, x m Is the same as the umbe of oegatve solutos of m.

6 6 y y m, y 0. oposto 6. ou text gves the esult m. pa of sx sded dce s thow utl a 8 o appea. What s the pobablty that a 8 appeas fst? 5/36 / /36 5/36 /36 /36 othe 9/36 Othe 9/36 othe 9/36 8 appeas fst appeas fst ( ) 5 36 ( ) k 5 most pob k

7 7 Now, Let us look at dstbutg balls to sum whe all the balls ae udstgushable. We kow dstgushable balls ca be dstbuted to possble us possble outcomes. Whe dstgushable balls ae used, the the balls put to us s descbed by the outcome; (,,... th x x x ), Whee x # balls ι sum So we have to fd the umbe of dstct, o-egatve, tege-valued vectos, (x,x, x ), such that; X x...x I ode to fd ths, suppose we fst have dstgushable objects led-up, ad we wat to dvde them to o-empty goups. 0x 0 x0 x... x 0x 0 object, 0, wth x deotg a space We eed to select - spaces of the - spaces, x, avalable betwee adjacet objects..e. If 8, 3, the the two dvdes ae 000/000/00 x, x 3, x 3,3, 3 3 ad the vecto s ( ) ( ) Sce thee ae possble selectos, we ca coclude. Theoem I Thee ae dstct postve tege-valued vectos ( x, x,... x ) satsfyg x x..., x x > 0,

8 8 To obta the umbe of o-egatve solutos (that s, to allow empty cells), the umbe of oegatve solutos of x x... x s detcal to the umbe of postve tege solutos of y y... y (Whch we ca see by lettg y x,,... ) Fom Theoem I, above, we get ( ) N, wth ( )( ) N d, Theoem II s Thee ae (-) dstct o-egatve tege valued vectos x x... x satsfyg x x... x ( ) h, How may ways ca we dstbute 3 black, dstgushable balls to two sum as alke the # of dstct o-egatve tege valued solutos of x x 3 ae possble. 3, ( 0,3)(, )(,)( 3.0) osde a vesto has $0,000 to vest amog 4 possble vestmets. How may dffeet vestmet stateges ae possble? If: ) all moey s to be vested ) ot all moey s to be vested. Soluto: Let x ι ;,, 3, 4, be the umbe of 000 s of dollas vested vestmet. a) The whe all moey s to be vested, x x x x 0 x ι Hece, whe

9 9 0, 4, possble stateges 3* * 3* 3* * 7 b) If ot all moey s to be vested, we let x 5 deote the amout kept eseve. x, x, x3, x4, x5 0, 5 I ths case, a stategy s a o-egatve tege-valued vecto ( ) osde a set of ateas, of whch m ae defectve ad -m ae fuctoal. ssume that the defectve ad all the fuctog ateas ae dstgushable. How may lea odeg ae thee whch o two defectve ateas ae cosecutve, m< m. Soluto: Image -m fuctoal ateas led up. 0-fuctoal ateas ϖ-defectve ateas -m Now, f o two defectos ae to be cosecutve, the the spaces betwee the fuctoal ateas must cota at most oe defectve atea That s the -m possble postos betwee the -m fuctoal ateas, we must select m of these to put the defectve ateas. Hece: m m < m These ae the possble odegs whch thee s at least oe fuctoal atea betwee two defectve oes.

10 0 Useful combato detty s;.e. osde object ad focus o object #. Now, thee ae combato of sze that cotas object. lso, thee ae combatos of sze that do ot cota object. Sce the ae combato of sze. QED oof: ( ) ( ) ( ) ( ) ( ) ( ) ( ) ( ) ( ) ( ) ( ) ( ) ( ) ( ) We ae ow eady to gve the fomal axomatc defto of pobablty. Let S be the set of all outcomes ε of a expemet. ε s a set (o sub set of S of evet pots. Hece we have a pobablty space. (S,F,) If we ca assg () obablty Measue of evet F such that the followg axoms wll be satsfed: ) ) ( 0

11 ) ( ) S - obablty of ceta evet s ) ( ) obablty of mpossble evet s 0 v) If 0 -o pots commo, the ( ) ( ) ( ) f 0 β ( ) ( ) ( ) ( ) We had two evets ad. ( ) ( ) ( ) ( ) ( ) ( ) ( ) ( ) ( ) ( ) ( ) ( ) ( ) ut, ( ) ( ) ( ) ( ) ( ) ( ) ( ) ( ) ( ) ( ) ( ) ( ) ( ) ( ) QED a a a c a a We ca exted to thee ad fou evets as follows, ( ) ( ) ( ) ( ) ( ) ( ) ( ) ( ) c a ( ) ( ) ( ) ( ) ( ) ( ) ( ) ( ) ( ) ( ) ( ) ( ) ( ) ( ) ( ) ( ) ( ) D D D D D D D D D Geealzg: ( ) ( ) ( ) ( ) ( ) ( ) L L L L L L L L < < <

12 The Summato: ( )... {,,,}. s take ove all possble subsets of sze of the set, osde tossg thee cos ad obsevg how they lad. The Space, S, ca be descbed as follows; st d 3d H H H H H T 3 H T T 4 T T T 5 H T H 6 T T H 7 T H H 8 T H T Let heads occu Fst co s a head {, 5, 7} {,, 3, 5} {, } 5 ssumg equal pobablty fo each eve ( ) ( )... 8 ( ) 3, ( ) 4, ( ) ( ) ( ) ( ) ( ) ( ) 5 8

13 3 u cotas balls, of whch oe s specal. If k of these balls ae wthdaw oe at a tme, wth each selecto beg equally lkely to be ay of the balls that em,a at the tme, what s the pobablty that the specal ball s chose? Soluto: Sce all the balls ae detcal, the the set of k balls ca be oe of the sets of k balls. So k k k {specal ball s selected} k Ths could also have bee obtaed by lettg deote the evet that the specal ball s the th ball to be chose,, k. The, sce each oe of the balls s equally lkely to be the th ball chose, t follows that ( )/. Hece, sce these evets ae clealy mutually exlusve, the k k k {specal ball s selected} ( ) It could have bee agued that ()/, by otg that thee ae ( ) ( k ) /( k) equally lkely outcomes of the expemet, of whch ( )( ) ( )()( ) ( k ) ( )/( k) specal ball beg the th oe chose. Fom ths t follows that ( ) ( ) esult the football team cossts of 0 offesve ad 0 defesve playes. The playes ae to be paed goups of fo the pupose of detemg oommates. If the pag s doe at adom, what s the pobablty that thee ae o offesve-defesve oommate pas? What d s the pobablty that thee ae offesve-defesve pas, I,, 0? Soluto We have 40 playe that must be paed oto goups of, Hece thee ae 40 (40) 0,,..., () ways od sotg the playes to odeed pas.(.e. thee s a fst pa, secod pa, etc) To get uodeed pas we smply dvde by 0. Now, a dvso wll esult o offesve-defesve pas f the offesve (ad cosequetly, the defesve) playes ae paed amog themselves. It should be (0) obvous that thee ae such dvso fo both offesve ad defesve 0 (0) playes. Hece, the pobabolty of o offesve-defesve pag, O, s

14 4 O (0) (0) [(0)] 0 3 (40) [(0)] (40) 0 (0) To deteme, the pobablty that thee ae offesve-defesve pas, we 0 ote thee ae ways of selectg the 4 playes volved ( offesve ad defesve). These 4 playes ca the be paed up () as. The emag 0- (offesve ad defesve) playes ae the paed amog themselves, t follows that thee ae 0 (0 ) ( ) 0 (0 ) dvsos whch lead to offesve-defesve pas. Hece 0 (0 ) ( ) 0 (0 ), 0,,...,0 (40) 0 (0) aothe example whch llustates the xoms of pobablty ad the mathematcal dextety eeded fo solvg poblems s as follows. Suppose that each of N me at a paty thows hs hat to the cete of th e oom, The hats ae fst mxed up ad the each ma adomly selects a hat. What s the pobablty that a) oe of the me selects hs ow hat; (b) exactly k of the me select the ow hat? Soluto: (a) We fst go the back doo ad calculate the pobablty of the complemetay evet of at least oe ma s selectg hs ow hat. Let E,,, N, be the evet that the th ma selects hs ow hat. Now by the geeal expesso fo the uo of N o-mutually exclusve evets, the pobablty that at least oe of the me selects hs owhat s ( ) ( EE ) E < N N E E ( ) EE ( ) ( ) ( ) EE E < < N ( ) EE ( EN )

15 5 osde ths expemet esultg a vecto whose elemets epeset the umbe of the hat take by the th ma. Fo example, the vecto (E E E ) s the vecto fo the evet that each ma selects hs ow hat.. The thee ae N possble such vectos Now, the evet that each of me,,,,, selects hs ow hat ca occu ay of (N-)x(N--)x x3xx(n-) possble ways, sce thee s oly oe way fo the me to select the ow hat, the, of the N- me emag, the fst ca select ay of (N-) hats, the secod ca select (N--), ad so o. So, assumg all N possbltes equally lkely, N ( N ) otug, thee ae tems EE { ) E, so N N( N ) EE ( ) E < < ( N ) N ad thus, N N E ( ) 3 N So, the pobablty that oe of the me selects hs ow hat s N ( ) 3 N d fo lage values of N, ths ca be appoxmated by e I othe wods, fo lage N, the pobablty that oe of the me selects hs ow hat s appoxmately.37. Note, t does ot go to fty, as mght be expected. (b) osde the evet that exactly k of the me select the ow hats, The umbe of ways that oly these k me ca select the ow hats s the sam as the umbe of ways whch the othe N- me ca select amog the emag hats such that oe of them select the ow hats. ut Nk ( ) s the pobablty we just foud fo ot 3 ( N k) oe of the N-k me selectg the ow hat.. The the umbe of ways whch the set of me selectg the ow hats s Nk ( N k) ( ) 3 ( N k)

16 6 Sce thee ae N ways to select k me fom N, Nk ( N k) ( ) 3 ( N k) {Exactly k me select the ow hats} N Nk ( ) 3 ( N k) k Fo lage N, ths ca be appoxmated by e. k

16. Mean Square Estimation

16. Mean Square Estimation 6 Me Sque stmto Gve some fomto tht s elted to uow qutty of teest the poblem s to obt good estmte fo the uow tems of the obseved dt Suppose epeset sequece of dom vbles bout whom oe set of obsevtos e vlble

More information

Randomized Load Balancing by Joining and Splitting Bins

Randomized Load Balancing by Joining and Splitting Bins Radomzed Load Baacg by Jog ad Spttg Bs James Aspes Ytog Y 1 Itoducto Cosde the foowg oad baacg sceao: a ceta amout of wo oad s dstbuted amog a set of maches that may chage ove tme as maches o ad eave the

More information

STATISTICAL PROPERTIES OF LEAST SQUARES ESTIMATORS. x, where. = y - ˆ " 1

STATISTICAL 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

Revenue Management for Online Advertising: Impatient Advertisers

Revenue Management for Online Advertising: Impatient Advertisers Reveue Maagemet fo Ole Advetsg: Impatet Advetses Kst Fdgesdott Maagemet Scece ad Opeatos, Lodo Busess School, Reget s Pak, Lodo, NW 4SA, Uted Kgdom, kst@lodo.edu Sam Naaf Asadolah Maagemet Scece ad Opeatos,

More information

ANOVA Notes Page 1. Analysis of Variance for a One-Way Classification of Data

ANOVA Notes Page 1. Analysis of Variance for a One-Way Classification of Data ANOVA Notes Page Aalss of Varace for a Oe-Wa Classfcato of Data Cosder a sgle factor or treatmet doe at levels (e, there are,, 3, dfferet varatos o the prescrbed treatmet) Wth a gve treatmet level there

More information

A Markov Chain Grey Forecasting Model: A Case Study of Energy Demand of Industry Sector in Iran

A Markov Chain Grey Forecasting Model: A Case Study of Energy Demand of Industry Sector in Iran 0 3d Iteatoal Cofeece o Ifomato ad Facal Egeeg IED vol. (0) (0) IACSIT ess, Sgapoe A Makov Cha Gey Foecastg Model: A Case Study of Eegy Demad of Idusty Secto Ia A. Kazem +, M. Modaes, M.. Mehega, N. Neshat

More information

Average Price Ratios

Average Price Ratios Average Prce Ratos Morgstar Methodology Paper August 3, 2005 2005 Morgstar, Ic. All rghts reserved. The formato ths documet s the property of Morgstar, Ic. Reproducto or trascrpto by ay meas, whole or

More information

Preprocess a planar map S. Given a query point p, report the face of S containing p. Goal: O(n)-size data structure that enables O(log n) query time.

Preprocess a planar map S. Given a query point p, report the face of S containing p. Goal: O(n)-size data structure that enables O(log n) query time. Computatoal Geometry Chapter 6 Pot Locato 1 Problem Defto Preprocess a plaar map S. Gve a query pot p, report the face of S cotag p. S Goal: O()-sze data structure that eables O(log ) query tme. C p E

More information

Simple Linear Regression

Simple Linear Regression Smple Lear Regresso Regresso equato a equato that descrbes the average relatoshp betwee a respose (depedet) ad a eplaator (depedet) varable. 6 8 Slope-tercept equato for a le m b (,6) slope. (,) 6 6 8

More information

10.5 Future Value and Present Value of a General Annuity Due

10.5 Future Value and Present Value of a General Annuity Due Chapter 10 Autes 371 5. Thomas leases a car worth $4,000 at.99% compouded mothly. He agrees to make 36 lease paymets of $330 each at the begg of every moth. What s the buyout prce (resdual value of the

More information

6.7 Network analysis. 6.7.1 Introduction. References - Network analysis. Topological analysis

6.7 Network analysis. 6.7.1 Introduction. References - Network analysis. Topological analysis 6.7 Network aalyss Le data that explctly store topologcal formato are called etwork data. Besdes spatal operatos, several methods of spatal aalyss are applcable to etwork data. Fgure: Network data Refereces

More information

Numerical Methods with MS Excel

Numerical Methods with MS Excel TMME, vol4, o.1, p.84 Numercal Methods wth MS Excel M. El-Gebely & B. Yushau 1 Departmet of Mathematcal Sceces Kg Fahd Uversty of Petroleum & Merals. Dhahra, Saud Araba. Abstract: I ths ote we show how

More information

Chapter 3 0.06 = 3000 ( 1.015 ( 1 ) Present Value of an Annuity. Section 4 Present Value of an Annuity; Amortization

Chapter 3 0.06 = 3000 ( 1.015 ( 1 ) Present Value of an Annuity. Section 4 Present Value of an Annuity; Amortization Chapter 3 Mathematcs of Face Secto 4 Preset Value of a Auty; Amortzato Preset Value of a Auty I ths secto, we wll address the problem of determg the amout that should be deposted to a accout ow at a gve

More information

EFFICIENT GENERATION OF CFD-BASED LOADS FOR THE FEM-ANALYSIS OF SHIP STRUCTURES

EFFICIENT GENERATION OF CFD-BASED LOADS FOR THE FEM-ANALYSIS OF SHIP STRUCTURES EFFICIENT GENERATION OF CFD-BASED LOADS FOR THE FEM-ANALYSIS OF SHIP STRUCTURES H Ese ad C Cabos, Gemasche Lloyd AG, Gemay SUMMARY Stegth aalyss of shp stuctues by meas of FEM eques ealstc loads. The most

More information

Finance Practice Problems

Finance Practice Problems Iteest Fiace Pactice Poblems Iteest is the cost of boowig moey. A iteest ate is the cost stated as a pecet of the amout boowed pe peiod of time, usually oe yea. The pevailig maket ate is composed of: 1.

More information

(Semi)Parametric Models vs Nonparametric Models

(Semi)Parametric Models vs Nonparametric Models buay, 2003 Pobablty Models (Sem)Paametc Models vs Nonpaametc Models I defne paametc, sempaametc, and nonpaametc models n the two sample settng My defnton of sempaametc models s a lttle stonge than some

More information

of the relationship between time and the value of money.

of the relationship between time and the value of money. TIME AND THE VALUE OF MONEY Most agrbusess maagers are famlar wth the terms compoudg, dscoutg, auty, ad captalzato. That s, most agrbusess maagers have a tutve uderstadg that each term mples some relatoshp

More information

How To Value An Annuity

How To Value An Annuity Future Value of a Auty After payg all your blls, you have $200 left each payday (at the ed of each moth) that you wll put to savgs order to save up a dow paymet for a house. If you vest ths moey at 5%

More information

Classic Problems at a Glance using the TVM Solver

Classic Problems at a Glance using the TVM Solver C H A P T E R 2 Classc Problems at a Glace usg the TVM Solver The table below llustrates the most commo types of classc face problems. The formulas are gve for each calculato. A bref troducto to usg the

More information

1. The Time Value of Money

1. The Time Value of Money Corporate Face [00-0345]. The Tme Value of Moey. Compoudg ad Dscoutg Captalzato (compoudg, fdg future values) s a process of movg a value forward tme. It yelds the future value gve the relevat compoudg

More information

Chapter Eight. f : R R

Chapter Eight. f : R R Chapter Eght f : R R 8. Itroducto We shall ow tur our atteto to the very mportat specal case of fuctos that are real, or scalar, valued. These are sometmes called scalar felds. I the very, but mportat,

More information

Chapter 3. AMORTIZATION OF LOAN. SINKING FUNDS R =

Chapter 3. AMORTIZATION OF LOAN. SINKING FUNDS R = Chapter 3. AMORTIZATION OF LOAN. SINKING FUNDS Objectves of the Topc: Beg able to formalse ad solve practcal ad mathematcal problems, whch the subjects of loa amortsato ad maagemet of cumulatve fuds are

More information

APPENDIX III THE ENVELOPE PROPERTY

APPENDIX III THE ENVELOPE PROPERTY Apped III APPENDIX III THE ENVELOPE PROPERTY Optmzato mposes a very strog structure o the problem cosdered Ths s the reaso why eoclasscal ecoomcs whch assumes optmzg behavour has bee the most successful

More information

Curve Fitting and Solution of Equation

Curve Fitting and Solution of Equation UNIT V Curve Fttg ad Soluto of Equato 5. CURVE FITTING I ma braches of appled mathematcs ad egeerg sceces we come across epermets ad problems, whch volve two varables. For eample, t s kow that the speed

More information

Answers to Warm-Up Exercises

Answers to Warm-Up Exercises Aswes to Wam-Up Execses E8-1. Total aual etu Aswe: ($0 $1,000 $10,000) $10,000 $,000 $10,000 0% Logstcs, Ic. doubled the aual ate of etu pedcted by the aalyst. The egatve et come s elevat to the poblem.

More information

Understanding Financial Management: A Practical Guide Guideline Answers to the Concept Check Questions

Understanding Financial Management: A Practical Guide Guideline Answers to the Concept Check Questions Udestadig Fiacial Maagemet: A Pactical Guide Guidelie Aswes to the Cocept Check Questios Chapte 4 The Time Value of Moey Cocept Check 4.. What is the meaig of the tems isk-etu tadeoff ad time value of

More information

An Effectiveness of Integrated Portfolio in Bancassurance

An Effectiveness of Integrated Portfolio in Bancassurance A Effectveess of Itegrated Portfolo Bacassurace Taea Karya Research Ceter for Facal Egeerg Isttute of Ecoomc Research Kyoto versty Sayouu Kyoto 606-850 Japa arya@eryoto-uacp Itroducto As s well ow the

More information

Investment Science Chapter 3

Investment Science Chapter 3 Ivestmet Scece Chapte 3 D. James. Tztzous 3. se P wth 7/.58%, P $5,, a 7 84, to obta $377.3. 3. Obseve that sce the et peset value of X s P, the cash flow steam ave at by cyclg X s equvalet

More information

Week 3-4: Permutations and Combinations

Week 3-4: Permutations and Combinations Week 3-4: Pemutations and Combinations Febuay 24, 2016 1 Two Counting Pinciples Addition Pinciple Let S 1, S 2,, S m be disjoint subsets of a finite set S If S S 1 S 2 S m, then S S 1 + S 2 + + S m Multiplication

More information

Opinion Makers Section

Opinion Makers Section Goupe de Taal Euopée Ade Multctèe à la Décso Euopea Wog Goup Multple Ctea Decso Adg Sée 3, º8, autome 008. Sees 3, º 8, Fall 008. Opo Maes Secto Hamozg poty weghts ad dffeece judgmets alue fucto mplemetato

More information

Money Math for Teens. Introduction to Earning Interest: 11th and 12th Grades Version

Money Math for Teens. Introduction to Earning Interest: 11th and 12th Grades Version Moey Math fo Tees Itoductio to Eaig Iteest: 11th ad 12th Gades Vesio This Moey Math fo Tees lesso is pat of a seies ceated by Geeatio Moey, a multimedia fiacial liteacy iitiative of the FINRA Ivesto Educatio

More information

The Binomial Distribution

The Binomial Distribution The Binomial Distibution A. It would be vey tedious if, evey time we had a slightly diffeent poblem, we had to detemine the pobability distibutions fom scatch. Luckily, thee ae enough similaities between

More information

The Time Value of Money

The Time Value of Money The Tme Value of Moey 1 Iversemet Optos Year: 1624 Property Traded: Mahatta Islad Prce : $24.00, FV of $24 @ 6%: FV = $24 (1+0.06) 388 = $158.08 bllo Opto 1 0 1 2 3 4 5 t ($519.37) 0 0 0 0 $1,000 Opto

More information

The Gompertz-Makeham distribution. Fredrik Norström. Supervisor: Yuri Belyaev

The Gompertz-Makeham distribution. Fredrik Norström. Supervisor: Yuri Belyaev The Gompertz-Makeham dstrbuto by Fredrk Norström Master s thess Mathematcal Statstcs, Umeå Uversty, 997 Supervsor: Yur Belyaev Abstract Ths work s about the Gompertz-Makeham dstrbuto. The dstrbuto has

More information

The simple linear Regression Model

The simple linear Regression Model The smple lear Regresso Model Correlato coeffcet s o-parametrc ad just dcates that two varables are assocated wth oe aother, but t does ot gve a deas of the kd of relatoshp. Regresso models help vestgatg

More information

MDM 4U PRACTICE EXAMINATION

MDM 4U PRACTICE EXAMINATION MDM 4U RCTICE EXMINTION Ths s a ractce eam. It does ot cover all the materal ths course ad should ot be the oly revew that you do rearato for your fal eam. Your eam may cota questos that do ot aear o ths

More information

n. We know that the sum of squares of p independent standard normal variables has a chi square distribution with p degrees of freedom.

n. We know that the sum of squares of p independent standard normal variables has a chi square distribution with p degrees of freedom. UMEÅ UNIVERSITET Matematsk-statstska sttutoe Multvarat dataaalys för tekologer MSTB0 PA TENTAMEN 004-0-9 LÖSNINGSFÖRSLAG TILL TENTAMEN I MATEMATISK STATISTIK Multvarat dataaalys för tekologer B, 5 poäg.

More information

Generalized Difference Sequence Space On Seminormed Space By Orlicz Function

Generalized Difference Sequence Space On Seminormed Space By Orlicz Function Ieaoa Joa of Scece ad Eee Reeach IJSER Vo Ie Decembe -4 5687 568X Geeazed Dffeece Seece Sace O Semomed Sace B Ocz Fco A.Sahaaa Aa ofeo G Ie of TechooCombaoeIda. Abac I h aewe defe he eece ace o emomed

More information

The analysis of annuities relies on the formula for geometric sums: r k = rn+1 1 r 1. (2.1) k=0

The analysis of annuities relies on the formula for geometric sums: r k = rn+1 1 r 1. (2.1) k=0 Chapter 2 Autes ad loas A auty s a sequece of paymets wth fxed frequecy. The term auty orgally referred to aual paymets (hece the ame), but t s ow also used for paymets wth ay frequecy. Autes appear may

More information

IDENTIFICATION OF THE DYNAMICS OF THE GOOGLE S RANKING ALGORITHM. A. Khaki Sedigh, Mehdi Roudaki

IDENTIFICATION OF THE DYNAMICS OF THE GOOGLE S RANKING ALGORITHM. A. Khaki Sedigh, Mehdi Roudaki IDENIFICAION OF HE DYNAMICS OF HE GOOGLE S RANKING ALGORIHM A. Khak Sedgh, Mehd Roudak Cotrol Dvso, Departmet of Electrcal Egeerg, K.N.oos Uversty of echology P. O. Box: 16315-1355, ehra, Ira sedgh@eetd.ktu.ac.r,

More information

Sequences and Series

Sequences and Series Secto 9. Sequeces d Seres You c thk of sequece s fucto whose dom s the set of postve tegers. f ( ), f (), f (),... f ( ),... Defto of Sequece A fte sequece s fucto whose dom s the set of postve tegers.

More information

Credibility Premium Calculation in Motor Third-Party Liability Insurance

Credibility Premium Calculation in Motor Third-Party Liability Insurance Advaces Mathematcal ad Computatoal Methods Credblty remum Calculato Motor Thrd-arty Lablty Isurace BOHA LIA, JAA KUBAOVÁ epartmet of Mathematcs ad Quattatve Methods Uversty of ardubce Studetská 95, 53

More information

Report 52 Fixed Maturity EUR Industrial Bond Funds

Report 52 Fixed Maturity EUR Industrial Bond Funds Rep52, Computed & Prted: 17/06/2015 11:53 Report 52 Fxed Maturty EUR Idustral Bod Fuds From Dec 2008 to Dec 2014 31/12/2008 31 December 1999 31/12/2014 Bechmark Noe Defto of the frm ad geeral formato:

More information

10/19/2011. Financial Mathematics. Lecture 24 Annuities. Ana NoraEvans 403 Kerchof AnaNEvans@virginia.edu http://people.virginia.

10/19/2011. Financial Mathematics. Lecture 24 Annuities. Ana NoraEvans 403 Kerchof AnaNEvans@virginia.edu http://people.virginia. Math 40 Lecture 24 Autes Facal Mathematcs How ready do you feel for the quz o Frday: A) Brg t o B) I wll be by Frday C) I eed aother week D) I eed aother moth Aa NoraEvas 403 Kerchof AaNEvas@vrga.edu http://people.vrga.edu/~as5k/

More information

Abraham Zaks. Technion I.I.T. Haifa ISRAEL. and. University of Haifa, Haifa ISRAEL. Abstract

Abraham Zaks. Technion I.I.T. Haifa ISRAEL. and. University of Haifa, Haifa ISRAEL. Abstract Preset Value of Autes Uder Radom Rates of Iterest By Abraham Zas Techo I.I.T. Hafa ISRAEL ad Uversty of Hafa, Hafa ISRAEL Abstract Some attempts were made to evaluate the future value (FV) of the expected

More information

CSSE463: Image Recognition Day 27

CSSE463: Image Recognition Day 27 CSSE463: Image Recogto Da 27 Ths week Toda: Alcatos of PCA Suda ght: roject las ad relm work due Questos? Prcal Comoets Aalss weght grth c ( )( ) ( )( ( )( ) ) heght sze Gve a set of samles, fd the drecto(s)

More information

A Study of Unrelated Parallel-Machine Scheduling with Deteriorating Maintenance Activities to Minimize the Total Completion Time

A Study of Unrelated Parallel-Machine Scheduling with Deteriorating Maintenance Activities to Minimize the Total Completion Time Joural of Na Ka, Vol. 0, No., pp.5-9 (20) 5 A Study of Urelated Parallel-Mache Schedulg wth Deteroratg Mateace Actvtes to Mze the Total Copleto Te Suh-Jeq Yag, Ja-Yuar Guo, Hs-Tao Lee Departet of Idustral

More information

An Algorithm For Factoring Integers

An Algorithm For Factoring Integers An Algothm Fo Factong Integes Yngpu Deng and Yanbn Pan Key Laboatoy of Mathematcs Mechanzaton, Academy of Mathematcs and Systems Scence, Chnese Academy of Scences, Bejng 100190, People s Republc of Chna

More information

Online Appendix: Measured Aggregate Gains from International Trade

Online Appendix: Measured Aggregate Gains from International Trade Ole Appedx: Measured Aggregate Gas from Iteratoal Trade Arel Burste UCLA ad NBER Javer Cravo Uversty of Mchga March 3, 2014 I ths ole appedx we derve addtoal results dscussed the paper. I the frst secto,

More information

CHAPTER 2. Time Value of Money 6-1

CHAPTER 2. Time Value of Money 6-1 CHAPTER 2 Tme Value of Moey 6- Tme Value of Moey (TVM) Tme Les Future value & Preset value Rates of retur Autes & Perpetutes Ueve cash Flow Streams Amortzato 6-2 Tme les 0 2 3 % CF 0 CF CF 2 CF 3 Show

More information

FINANCIAL MATHEMATICS 12 MARCH 2014

FINANCIAL MATHEMATICS 12 MARCH 2014 FINNCIL MTHEMTICS 12 MRCH 2014 I ths lesso we: Lesso Descrpto Make use of logarthms to calculate the value of, the tme perod, the equato P1 or P1. Solve problems volvg preset value ad future value autes.

More information

Statistical Pattern Recognition (CE-725) Department of Computer Engineering Sharif University of Technology

Statistical Pattern Recognition (CE-725) Department of Computer Engineering Sharif University of Technology I The Name of God, The Compassoate, The ercful Name: Problems' eys Studet ID#:. Statstcal Patter Recogto (CE-725) Departmet of Computer Egeerg Sharf Uversty of Techology Fal Exam Soluto - Sprg 202 (50

More information

On formula to compute primes and the n th prime

On formula to compute primes and the n th prime Joural's Ttle, Vol., 00, o., - O formula to compute prmes ad the th prme Issam Kaddoura Lebaese Iteratoal Uversty Faculty of Arts ad ceces, Lebao Emal: ssam.addoura@lu.edu.lb amh Abdul-Nab Lebaese Iteratoal

More information

ADAPTATION OF SHAPIRO-WILK TEST TO THE CASE OF KNOWN MEAN

ADAPTATION OF SHAPIRO-WILK TEST TO THE CASE OF KNOWN MEAN Colloquum Bometrcum 4 ADAPTATION OF SHAPIRO-WILK TEST TO THE CASE OF KNOWN MEAN Zofa Hausz, Joaa Tarasńska Departmet of Appled Mathematcs ad Computer Scece Uversty of Lfe Sceces Lubl Akademcka 3, -95 Lubl

More information

An Approach of Degree Constraint MST Algorithm

An Approach of Degree Constraint MST Algorithm I.J. Ifomato Techology a Compute Scece, 203, 09, 80-86 Publhe Ole Augut 203 MECS (http://www.mec-pe.og/) DOI: 0.585/jtc.203.09.08 A Appoach Degee Cotat MST Algothm Sajay Kuma Pal Depatmet Compute Sc. a

More information

Derivation of Annuity and Perpetuity Formulae. A. Present Value of an Annuity (Deferred Payment or Ordinary Annuity)

Derivation of Annuity and Perpetuity Formulae. A. Present Value of an Annuity (Deferred Payment or Ordinary Annuity) Aity Deivatios 4/4/ Deivatio of Aity ad Pepetity Fomlae A. Peset Vale of a Aity (Defeed Paymet o Odiay Aity 3 4 We have i the show i the lecte otes ad i ompodi ad Discoti that the peset vale of a set of

More information

3. Greatest Common Divisor - Least Common Multiple

3. Greatest Common Divisor - Least Common Multiple 3 Greatest Commo Divisor - Least Commo Multiple Defiitio 31: The greatest commo divisor of two atural umbers a ad b is the largest atural umber c which divides both a ad b We deote the greatest commo gcd

More information

Continuous Compounding and Annualization

Continuous Compounding and Annualization Continuous Compounding and Annualization Philip A. Viton Januay 11, 2006 Contents 1 Intoduction 1 2 Continuous Compounding 2 3 Pesent Value with Continuous Compounding 4 4 Annualization 5 5 A Special Poblem

More information

Finite-Difference-Frequency-Domain Simulation of Electrically Large Microwave Structures using PML and Internal Ports

Finite-Difference-Frequency-Domain Simulation of Electrically Large Microwave Structures using PML and Internal Ports Fte-ffeece-Fequecy-oa Sulato of Electcally Lage Mcowave Stuctues usg PML ad teal Pots vogelegt vo MSc Eg Podyut Kua Talukde aus Netakoa Bagladesh vo de Fakultät V - Elektotechk ud foatk - de Techsche Uvestät

More information

SIMULATION OF THE FLOW AND ACOUSTIC FIELD OF A FAN

SIMULATION OF THE FLOW AND ACOUSTIC FIELD OF A FAN Cofeece o Modellg Flud Flow (CMFF 9) The 14 th Iteatoal Cofeece o Flud Flow Techologes Budapest, Hugay, Septembe 9-1, 9 SIMULATION OF THE FLOW AND ACOUSTIC FIELD OF A FAN Q Wag 1, Mchael Hess, Bethold

More information

Common p-belief: The General Case

Common p-belief: The General Case GAMES AND ECONOMIC BEHAVIOR 8, 738 997 ARTICLE NO. GA97053 Commo p-belef: The Geeral Case Atsush Kaj* ad Stephe Morrs Departmet of Ecoomcs, Uersty of Pesylaa Receved February, 995 We develop belef operators

More information

Cyber Journals: Multidisciplinary Journals in Science and Technology, Journal of Selected Areas in Telecommunications (JSAT), January Edition, 2011

Cyber Journals: Multidisciplinary Journals in Science and Technology, Journal of Selected Areas in Telecommunications (JSAT), January Edition, 2011 Cyber Jourals: Multdscplary Jourals cece ad Techology, Joural of elected Areas Telecommucatos (JAT), Jauary dto, 2011 A ovel rtual etwork Mappg Algorthm for Cost Mmzg ZHAG hu-l, QIU Xue-sog tate Key Laboratory

More information

CS103X: Discrete Structures Homework 4 Solutions

CS103X: Discrete Structures Homework 4 Solutions CS103X: Discrete Structures Homewor 4 Solutios Due February 22, 2008 Exercise 1 10 poits. Silico Valley questios: a How may possible six-figure salaries i whole dollar amouts are there that cotai at least

More information

RUSSIAN ROULETTE AND PARTICLE SPLITTING

RUSSIAN ROULETTE AND PARTICLE SPLITTING RUSSAN ROULETTE AND PARTCLE SPLTTNG M. Ragheb 3/7/203 NTRODUCTON To stuatos are ecoutered partcle trasport smulatos:. a multplyg medum, a partcle such as a eutro a cosmc ray partcle or a photo may geerate

More information

Security Analysis of RAPP: An RFID Authentication Protocol based on Permutation

Security Analysis of RAPP: An RFID Authentication Protocol based on Permutation Securty Aalyss of RAPP: A RFID Authetcato Protocol based o Permutato Wag Shao-hu,,, Ha Zhje,, Lu Sujua,, Che Da-we, {College of Computer, Najg Uversty of Posts ad Telecommucatos, Najg 004, Cha Jagsu Hgh

More information

N V V L. R a L I. Transformer Equation Notes

N V V L. R a L I. Transformer Equation Notes Tnsfome Eqution otes This file conts moe etile eivtion of the tnsfome equtions thn the notes o the expeiment 3 wite-up. t will help you to unestn wht ssumptions wee neee while eivg the iel tnsfome equtions

More information

Models for Selecting an ERP System with Intuitionistic Trapezoidal Fuzzy Information

Models for Selecting an ERP System with Intuitionistic Trapezoidal Fuzzy Information JOURNAL OF SOFWARE, VOL 5, NO 3, MARCH 00 75 Models for Selectg a ERP System wth Itutostc rapezodal Fuzzy Iformato Guwu We, Ru L Departmet of Ecoomcs ad Maagemet, Chogqg Uversty of Arts ad Sceces, Yogchua,

More information

Relaxation Methods for Iterative Solution to Linear Systems of Equations

Relaxation Methods for Iterative Solution to Linear Systems of Equations Relaxato Methods for Iteratve Soluto to Lear Systems of Equatos Gerald Recktewald Portlad State Uversty Mechacal Egeerg Departmet gerry@me.pdx.edu Prmary Topcs Basc Cocepts Statoary Methods a.k.a. Relaxato

More information

Gravitation. Definition of Weight Revisited. Newton s Law of Universal Gravitation. Newton s Law of Universal Gravitation. Gravitational Field

Gravitation. Definition of Weight Revisited. Newton s Law of Universal Gravitation. Newton s Law of Universal Gravitation. Gravitational Field Defnton of Weght evsted Gavtaton The weght of an object on o above the eath s the gavtatonal foce that the eath exets on the object. The weght always ponts towad the cente of mass of the eath. On o above

More information

Speeding up k-means Clustering by Bootstrap Averaging

Speeding up k-means Clustering by Bootstrap Averaging Speedg up -meas Clusterg by Bootstrap Averagg Ia Davdso ad Ashw Satyaarayaa Computer Scece Dept, SUNY Albay, NY, USA,. {davdso, ashw}@cs.albay.edu Abstract K-meas clusterg s oe of the most popular clusterg

More information

Optimizing Multiproduct Multiconstraint Inventory Control Systems with Stochastic Period Length and Emergency Order

Optimizing Multiproduct Multiconstraint Inventory Control Systems with Stochastic Period Length and Emergency Order 585858585814 Joual of Uceta Systes Vol.7, No.1, pp.58-71, 013 Ole at: www.us.og.uk Optzg Multpoduct Multcostat Ivetoy Cotol Systes wth Stochastc Peod Legth ad egecy Ode Ata Allah Talezadeh 1, Seyed Tagh

More information

Optimal multi-degree reduction of Bézier curves with constraints of endpoints continuity

Optimal multi-degree reduction of Bézier curves with constraints of endpoints continuity Computer Aded Geometrc Desg 19 (2002 365 377 wwwelsevercom/locate/comad Optmal mult-degree reducto of Bézer curves wth costrats of edpots cotuty Guo-Dog Che, Guo-J Wag State Key Laboratory of CAD&CG, Isttute

More information

T = 1/freq, T = 2/freq, T = i/freq, T = n (number of cash flows = freq n) are :

T = 1/freq, T = 2/freq, T = i/freq, T = n (number of cash flows = freq n) are : Bullets bods Let s descrbe frst a fxed rate bod wthout amortzg a more geeral way : Let s ote : C the aual fxed rate t s a percetage N the otoal freq ( 2 4 ) the umber of coupo per year R the redempto of

More information

Lecture 7. Norms and Condition Numbers

Lecture 7. Norms and Condition Numbers Lecture 7 Norms ad Codto Numbers To dscuss the errors umerca probems vovg vectors, t s usefu to empo orms. Vector Norm O a vector space V, a orm s a fucto from V to the set of o-egatve reas that obes three

More information

Chapter 3. Elementary statistical concepts. Summary. 3.1 Introductory comments

Chapter 3. Elementary statistical concepts. Summary. 3.1 Introductory comments Chapte 3 Elemetay statstcal cocepts Demets Kotsoyas Depatmet of Wate Resoces ad Evometal Egeeg Faclty of Cvl Egeeg, Natoal Techcal Uvesty of Athes, Geece mmay Ths chapte ams to seve as a emde ad syopss

More information

Capacitated Production Planning and Inventory Control when Demand is Unpredictable for Most Items: The No B/C Strategy

Capacitated Production Planning and Inventory Control when Demand is Unpredictable for Most Items: The No B/C Strategy SCHOOL OF OPERATIONS RESEARCH AND INDUSTRIAL ENGINEERING COLLEGE OF ENGINEERING CORNELL UNIVERSITY ITHACA, NY 4853-380 TECHNICAL REPORT Jue 200 Capactated Producto Plag ad Ivetory Cotrol whe Demad s Upredctable

More information

The dinner table problem: the rectangular case

The dinner table problem: the rectangular case The ie table poblem: the ectagula case axiv:math/009v [mathco] Jul 00 Itouctio Robeto Tauaso Dipatimeto i Matematica Uivesità i Roma To Vegata 00 Roma, Italy tauaso@matuiomait Decembe, 0 Assume that people

More information

Bayesian Network Representation

Bayesian Network Representation Readgs: K&F 3., 3.2, 3.3, 3.4. Bayesa Network Represetato Lecture 2 Mar 30, 20 CSE 55, Statstcal Methods, Sprg 20 Istructor: Su-I Lee Uversty of Washgto, Seattle Last tme & today Last tme Probablty theory

More information

Fractal-Structured Karatsuba`s Algorithm for Binary Field Multiplication: FK

Fractal-Structured Karatsuba`s Algorithm for Binary Field Multiplication: FK Fractal-Structured Karatsuba`s Algorthm for Bary Feld Multplcato: FK *The authors are worg at the Isttute of Mathematcs The Academy of Sceces of DPR Korea. **Address : U Jog dstrct Kwahadog Number Pyogyag

More information

On Error Detection with Block Codes

On Error Detection with Block Codes BULGARIAN ACADEMY OF SCIENCES CYBERNETICS AND INFORMATION TECHNOLOGIES Volume 9, No 3 Sofa 2009 O Error Detecto wth Block Codes Rostza Doduekova Chalmers Uversty of Techology ad the Uversty of Gotheburg,

More information

Statistical Decision Theory: Concepts, Methods and Applications. (Special topics in Probabilistic Graphical Models)

Statistical Decision Theory: Concepts, Methods and Applications. (Special topics in Probabilistic Graphical Models) Statstcal Decso Theory: Cocepts, Methods ad Applcatos (Specal topcs Probablstc Graphcal Models) FIRST COMPLETE DRAFT November 30, 003 Supervsor: Professor J. Rosethal STA4000Y Aal Mazumder 9506380 Part

More information

1. C. The formula for the confidence interval for a population mean is: x t, which was

1. C. The formula for the confidence interval for a population mean is: x t, which was s 1. C. The formula for the cofidece iterval for a populatio mea is: x t, which was based o the sample Mea. So, x is guarateed to be i the iterval you form.. D. Use the rule : p-value

More information

A Parallel Transmission Remote Backup System

A Parallel Transmission Remote Backup System 2012 2d Iteratoal Coferece o Idustral Techology ad Maagemet (ICITM 2012) IPCSIT vol 49 (2012) (2012) IACSIT Press, Sgapore DOI: 107763/IPCSIT2012V495 2 A Parallel Trasmsso Remote Backup System Che Yu College

More information

DECISION MAKING WITH THE OWA OPERATOR IN SPORT MANAGEMENT

DECISION MAKING WITH THE OWA OPERATOR IN SPORT MANAGEMENT ESTYLF08, Cuecas Meras (Meres - Lagreo), 7-9 de Septembre de 2008 DECISION MAKING WITH THE OWA OPERATOR IN SPORT MANAGEMENT José M. Mergó Aa M. Gl-Lafuete Departmet of Busess Admstrato, Uversty of Barceloa

More information

Approximation Algorithms for Scheduling with Rejection on Two Unrelated Parallel Machines

Approximation Algorithms for Scheduling with Rejection on Two Unrelated Parallel Machines (ICS) Iteratoal oural of dvaced Comuter Scece ad lcatos Vol 6 No 05 romato lgorthms for Schedulg wth eecto o wo Urelated Parallel aches Feg Xahao Zhag Zega Ca College of Scece y Uversty y Shadog Cha 76005

More information

Numerical Comparisons of Quality Control Charts for Variables

Numerical Comparisons of Quality Control Charts for Variables Global Vrtual Coferece Aprl, 8. - 2. 203 Nuercal Coparsos of Qualty Cotrol Charts for Varables J.F. Muñoz-Rosas, M.N. Pérez-Aróstegu Uversty of Graada Facultad de Cecas Ecoócas y Epresarales Graada, pa

More information

Finite Dimensional Vector Spaces.

Finite Dimensional Vector Spaces. Lctur 5. Ft Dmsoal Vctor Spacs. To b rad to th musc of th group Spac by D.Maruay DEFINITION OF A LINEAR SPACE Dfto: a vctor spac s a st R togthr wth a oprato calld vctor addto ad aothr oprato calld scalar

More information

On Savings Accounts in Semimartingale Term Structure Models

On Savings Accounts in Semimartingale Term Structure Models O Savgs Accouts Semmartgale Term Structure Models Frak Döberle Mart Schwezer moeyshelf.com Techsche Uverstät Berl Bockehemer Ladstraße 55 Fachberech Mathematk, MA 7 4 D 6325 Frakfurt am Ma Straße des 17.

More information

The Analysis of Development of Insurance Contract Premiums of General Liability Insurance in the Business Insurance Risk

The Analysis of Development of Insurance Contract Premiums of General Liability Insurance in the Business Insurance Risk The Aalyss of Developmet of Isurace Cotract Premums of Geeral Lablty Isurace the Busess Isurace Rsk the Frame of the Czech Isurace Market 1998 011 Scetfc Coferece Jue, 10. - 14. 013 Pavla Kubová Departmet

More information

Automated Event Registration System in Corporation

Automated Event Registration System in Corporation teratoal Joural of Advaces Computer Scece ad Techology JACST), Vol., No., Pages : 0-0 0) Specal ssue of CACST 0 - Held durg 09-0 May, 0 Malaysa Automated Evet Regstrato System Corporato Zafer Al-Makhadmee

More information

ANNEX 77 FINANCE MANAGEMENT. (Working material) Chief Actuary Prof. Gaida Pettere BTA INSURANCE COMPANY SE

ANNEX 77 FINANCE MANAGEMENT. (Working material) Chief Actuary Prof. Gaida Pettere BTA INSURANCE COMPANY SE ANNEX 77 FINANCE MANAGEMENT (Workg materal) Chef Actuary Prof. Gada Pettere BTA INSURANCE COMPANY SE 1 FUNDAMENTALS of INVESTMENT I THEORY OF INTEREST RATES 1.1 ACCUMULATION Iterest may be regarded as

More information

A CPN-based Trust Negotiation Model on Service Level Agreement in Cloud Environment

A CPN-based Trust Negotiation Model on Service Level Agreement in Cloud Environment , pp.247-258 http://dx.do.og/10.14257/jgdc.2015.8.2.22 A CPN-based Tust Negotato Model o Sevce Level Ageemet Cloud Evomet Hogwe Che, Quxa Che ad Chuzh Wag School of Compute Scece, Hube Uvesty of Techology,

More information

Infinite Sequences and Series

Infinite Sequences and Series CHAPTER 4 Ifiite Sequeces ad Series 4.1. Sequeces A sequece is a ifiite ordered list of umbers, for example the sequece of odd positive itegers: 1, 3, 5, 7, 9, 11, 13, 15, 17, 19, 21, 23, 25, 27, 29...

More information

THE PRINCIPLE OF THE ACTIVE JMC SCATTERER. Seppo Uosukainen

THE PRINCIPLE OF THE ACTIVE JMC SCATTERER. Seppo Uosukainen THE PRINCIPLE OF THE ACTIVE JC SCATTERER Seppo Uoukaie VTT Buildig ad Tapot Ai Hadlig Techology ad Acoutic P. O. Bo 1803, FIN 02044 VTT, Filad Seppo.Uoukaie@vtt.fi ABSTRACT The piciple of fomulatig the

More information

Elementary Theory of Russian Roulette

Elementary Theory of Russian Roulette Elemetary Theory of Russia Roulette -iterestig patters of fractios- Satoshi Hashiba Daisuke Miematsu Ryohei Miyadera Itroductio. Today we are goig to study mathematical theory of Russia roulette. If some

More information

The Digital Signature Scheme MQQ-SIG

The Digital Signature Scheme MQQ-SIG The Dgtal Sgature Scheme MQQ-SIG Itellectual Property Statemet ad Techcal Descrpto Frst publshed: 10 October 2010, Last update: 20 December 2010 Dalo Glgorosk 1 ad Rue Stesmo Ødegård 2 ad Rue Erled Jese

More information

Paper SD-07. Key words: upper tolerance limit, macros, order statistics, sample size, confidence, coverage, binomial

Paper SD-07. Key words: upper tolerance limit, macros, order statistics, sample size, confidence, coverage, binomial SESUG 212 Pae SD-7 Samle Size Detemiatio fo a Noaametic Ue Toleace Limit fo ay Ode Statistic D. Deis Beal, Sciece Alicatios Iteatioal Cooatio, Oak Ridge, Teessee ABSTRACT A oaametic ue toleace limit (UTL)

More information

CH. V ME256 STATICS Center of Gravity, Centroid, and Moment of Inertia CENTER OF GRAVITY AND CENTROID

CH. V ME256 STATICS Center of Gravity, Centroid, and Moment of Inertia CENTER OF GRAVITY AND CENTROID CH. ME56 STTICS Ceter of Gravt, Cetrod, ad Momet of Ierta CENTE OF GITY ND CENTOID 5. CENTE OF GITY ND CENTE OF MSS FO SYSTEM OF PTICES Ceter of Gravt. The ceter of gravt G s a pot whch locates the resultat

More information

Mathematics of Finance

Mathematics of Finance CATE Mathematcs of ace.. TODUCTO ths chapter we wll dscuss mathematcal methods ad formulae whch are helpful busess ad persoal face. Oe of the fudametal cocepts the mathematcs of face s the tme value of

More information

Integrated Workforce Planning Considering Regular and Overtime Decisions

Integrated Workforce Planning Considering Regular and Overtime Decisions Poceedgs of the 2011 Idusta Egeeg Reseach Cofeece T. Dooe ad E. Va Ae, eds. Itegated Wofoce Pag Cosdeg Regua ad Ovetme Decsos Shat Jaugum Depatmet of Egeeg Maagemet & Systems Egeeg Mssou Uvesty of Scece

More information