! ' I I I t | | | It | | t | (Biatisteal Dacision “FEetium q Reoblem of nek ng decisleng a& causing inptence about pep” poameke. “et 5 toran vets. knouutedae. obeut the uncertainty at Ex: The av volue af something may be hitfueent in " one aid) te ofhee- ler mates aoe have higher HB values, 00 A than Les + = The eferk of gnde en HB by the | chipperence in mean moles and females fn protitical infrrence, me dtuuly tue type blews He be? peo’ . peal 4 Tuting of Mapetiet Extimation ay Problem oF ; a both = the preblery , we colour conclusions, ob make letizian about the Popes ee paren eft an the — burp of Sampleboete. oe ceturekr suonts 10 clecile usher the 40t produced +heutdis be gent to the matket ce not. He é olecidl eg it on the = bawit op pope $ 4 Hy: p< bo ue uthith wan be terted on the bous oy TRE manufactueee selects dompie | laf ectives « Ao, he paperms one of sre floni g Be tead the dat tp ate mental ip eG propecia ipl of clefectins i the det is Sfp wheet po ia predetermined mo Cie Spo 7 send the ter) } Q, + Do not send the dot if p> be 4 | ! | oe futing oF Hegpetheeis problem (an be Considered as a Sfatdrtical Deciston faobLem-Ex : Contidte a joerree uhse hos sour home eds and he wants 48 frow how m them i eC ae ah ace ¥ obalaiey sample of ©n? seeds wand ausdate tv x such that R=] 5 cd the wed in the sample fa ° 5 IL the seed in the sample Last Fe inate “Tr enema 40 he hos we seceapsbe, geguence 5 § oa for the sample of aceds. The prepertin of feeds sahich eeminate can be tetimealid frem the sample waning the sample pepetion Jeo b lem oF ethrotin fe tonsiclered 4Describe Carmpenerts oft ae Detlsjon Peshl, Cot decivien blem , 2k ee howe the prctuledge, of Otto wing hogs “f ehements fu Components f a ms Peck lems @ 2 = Patametele space + Et the 4h gp at perible values of param chet: © A= Actian spoce + THU sre ot F powible ry aa d(xy whee d denates the clecisfen function © = Semple que ee ME tT “al epthle valucs Laker sack si ts, si ins Nr i es £ ¢ aa AL Vanable %- the [oe @® Lioa) > bow Fen whine B anocated +” my “ er ap the eT pncian meaty 7 atrin 44 fpr ye oO Or ---- a, L06) a) LC, a1) ~ ~*~ LE_ au} ae 1 1 \ as fo 8 ben anQ Explain the tus 5 deckiton pasklem. | Sfprecches The dedtien peoblem can be studied suing btpeent Approaches - a of States infomation make clecirion about pop? pasam hie — The sam infotmetion 41 combined with other — eleven asped +h the problem 79 make the 7 beck — ol etinion. @ Baysino Approach + Tn ss approach @ is Consiclertd as randem variable -and it & net probabilities ate vousaciated uith tv 0. @ Apeioes Peebabit i The péebabitity which i known, before drawing dample is tolled apeied eobabi lity. This eobedbility ods not end upen de ately denoted gle) wand itis anteined © Posterioe Probability? Th probability 4 obtained by combining the sample fnjesmation with the ‘ ebabili cand ince this ptobabil ar Pike pres de Sample if i eid we pasteuios probability and dé is denoted Wy §C8/x).bn cadditien te dam in, ation tuo athese ® ° 0 Ge af (ten given nt teins of lost each pastible ceceien (i) The aucond £auece nan sample information post exp lence bout gimilat gitvakians iniblving similoz 6° d The taw material foe gfatirtical invert ation is the eet of phsetvot'ens thi. ate, the Voted teLken the ev ew. the ws ef dut if 4 Krenn of Zz Ww an ulement ef ensun of astrs Pe any element of P -can be, denated by fy 1 G6 ushers -2 le the.eo mag be re single porametee 06 i may i Tngrtence deaty usith hour beak ate con wae the dat, %0 chtained the. information pout the unknown dit? 2. ghee. 0 which Lebels the obiat” qOEE Bhee | eusion Aule» TREE ake, a4 tes snd ue hove f dele the ra 7a aa fea oes ane 2 CE Lissa) 20 eco aad — +) e “pne_at & fee GQ Risk Function glicce hose fe whe. Zander -auantity at 3 based on. at alas bes 'veltiee WV —aaestegt ot expected: Aou in 0 on. A adllx) fo the lap : bunt) and Hal Le called as At) a Chuy the ed VALLE Lye. wh teh erBoth at Both fespuentist wand Boyeian ive U deutien bared on re _ expeckee Leet volue oy ’ tox chien However , this quantity Yas detined Aifpeently . one hauld choose HL pelian ip minimizer the accepted ose - ) Bayesian Expected Lose: 4 ue DHE expected Aows considering uncertainty 8 gine 9 Bb a tv © it unknolunat the AE sty making clecitions - TE $8) as the probabhity jay var of 6 ot the time oe decision see Then « Pan expected doug ip a peli (a! ib given ye Rlo,ay= E [cea] = j ae age) | Leen) Sted > Cort CHR = Le@,a) §Coe) z mee dist core duppace the Loss fonction 4s given 4 ® [= =] Oz | tooo = - 30°. and pee dit? af © a 800) = 0-9 Ge daae n= = - To find out the tek function Rlqjad =ELC@,ad Sco = Uo, a) SOF LOO, %) F Cop) = @g (-500) *O-9 + (-300) *o4 = -460-2T0 =~ 7120 RU : 625%) = LO, 19) $C) + Lb (G21) SCs) = OLpeo # LOL ee (e320) F Ol oS 4® Feequentist Risk Function 3 Here , the expected dors tp howd ott the 44%, ue have ned d= a(x) os the decision tute urhich depen en clined 24 ot = 5 [ eco, dz] z . a ~~ PENTEL e = = L(0,dw) fe OD 2 us con. & UY deen poraion da defined as dee & x = In thu appa a i ey hore dah ig fe) & & , coy ese Oo foeeen fee . ee ee Az fayet - € ooo! 7s aw th prob. 3ly $ < 4 ©=-G& » X= ee pred Aly : find Rsk Function i 9 a € o aly ‘Iq 3 Peobability matesx é Or [4 ly é { t {Fat we will hove pane dipper Aeciain sabes a4 ° (=f) ; Z p Oo © “\ then, = ide) Fed Ri den) = & [ Leo, dew) J 2 z Lee ee) Pe The porte decivins are RY} 9, ow a, _ S03 Fok Motu R(@ d= ELE Po . a = LLo, d C2] Po %) + LCS d(x) 7 R @ [it a oe "4 8h S IXE HT RIXE =IRie, du) = : = L(G 5di) Py (2) LL, dy Cu) Py ,(%2 tL6O, 9, (4) Pe 6%) a 7 oa R(8, , 4) = z L (0, ,d2) fe = LLG, ACK] Po, (4) + £00, A) OH? . zx! + + no a R(6,,43) = = L106, 45) fe, 0%) * i RG, 9 dy) = Eble, ) Fo, Qa Ra, di) =3 : RO, d) = Bs Resch I= 1B , R (62544) = 4 dud. da Risk Function = 61 d Sly Hy 2 6, [3 hy Be ¥ays I 4 a See The. wofnt that A dpe fite A 4D he Ate Oe datas, ie Ly a Lag i clipfet ence bef = ‘ ear es Value ®and extimates ALhSh vel - Mike 29_ aoe + Poatble wd =a woth dd Sa ie! Irises t hii cry commen exam le fovolres "docatien" andy Ay pica Pf asesinoptions the. "mean. 6 CA he APodistic foe i —echenll | ZhaOd mob mofecs — [eHan Speke ve tenced undee the Yu ated Shige lost has wiAfle medtan |: the erhtenahe thot . pop Abts undef alu clitt. MHONCE Lidid function. = Udi} fekent el timate a sesld he satin undes other —deps ADLON On TU teowmdtances + ec it 7 fy the renter! 4 2lenorntry Abit wth Que wi ually eLenemie Catt ge. ; a 7 = segted dD thawifs VON y Wa phe _ evynally puted io esh'mation ptallems- fh alt Anh fared eshimatees JO tuere being conutdlebod. since thea — the eupscte (VOLE @ the dott_l: - Ela-o')* youl pb the vardnce | Of. the__tutiona tee =a eis Rte.) =(2) linen lost Funstibnr The oa the oo ie eas at HCA = 6) 5, a ip ae ___ difierent Ms ——eguol, th. : aie Haste Lost lau = The Matas la ie ie bee = 40, 1a — _We denous “ane cu) Logsi fo the : testing. of othe 219 € ke Hit eer pat ated — dat —faiiicia. Lobia Gp = Accent Hy L ay = Refick ot accent H, Le 0p oe ty im Lesa) =0 if e ¢ 0, eo e.g Sah fet tact the Bick funda i fo > Rea) =f lea] 1g BE EE Pasbl adion oxeecd]- im 1x Paebl faction #1 ej2 cases = = — j as os the mena : Saat dunt rae foe path all neon 2Lanclom: ed of L ae apna Sart ah a wahen RCO Wdee)) ar dy oe pone 4 } Uni'feem Betfee Detiaien Pubes 4 detiFian sue di cr aid tebe uni ptoly bethut than decirion sule d,| He ROADS R18, ob) V6 6.0. Land. Rl idi) < Req) sad beast por one 6 + entimum Decl ion. pule declrion tule dt ed le gaid to be te oo “ yunthen ee, =(6=a)* _! _ R(@a) = i Thi kick wi be min® > ed A=O ee _ the. — opti? deeiao eile. enitls : L p= $8,,0 =£8),02 aie and fet _o= SO a tas Cs tule and tb OM, i SO, uk tee — et dan ith the a. / Yelk