LEADER 01021nam--2200361---450 001 990001207810203316 005 20200331092149.0 010 $a88-08-09051-5 035 $a000120781 035 $aUSA01000120781 035 $a(ALEPH)000120781USA01 035 $a000120781 100 $a20031021d2000----km-y0itay0103----ba 101 $aita 102 $aIT 105 $a||||||||001yy 200 1 $aRicettario di Scrittura creativa$fStefano Brugnolo e Giulio Mozzi 205 $aSeconda edizione in volume unico riveduta e aumentata 210 $aBologna$cZanichelli$d2000 215 $a479 p.$d24 cm 410 0$12001 461 1$1001-------$12001 606 0 $aLingua italiana 676 $a808.0451 700 1$aBRUGNOLO,$bStefano$0220808 701 1$aMOZZI,$bGiulio$0458942 801 0$aIT$bsalbc$gISBD 912 $a990001207810203316 951 $aXVII A.A. 753$b4281 DLAS$cXVII A.A.$d427202 959 $aBK 969 $aCAS 996 $aRicettario di scrittura creativa$9889488 997 $aUNISA LEADER 05463nam 2200697Ia 450 001 9910462657803321 005 20200520144314.0 010 $a1-280-77576-9 010 $a9786613686152 010 $a1-118-35975-5 035 $a(CKB)2670000000206484 035 $a(EBL)945113 035 $a(OCoLC)796383239 035 $a(SSID)ssj0000676781 035 $a(PQKBManifestationID)11415815 035 $a(PQKBTitleCode)TC0000676781 035 $a(PQKBWorkID)10683718 035 $a(PQKB)11778240 035 $a(MiAaPQ)EBC945113 035 $a(JP-MeL)3000065417 035 $a(CaSebORM)9781118359778 035 $a(PPN)164315020 035 $a(Au-PeEL)EBL945113 035 $a(CaPaEBR)ebr10570748 035 $a(CaONFJC)MIL368615 035 $a(EXLCZ)992670000000206484 100 $a20120228d2012 uy 0 101 0 $aeng 135 $aur|n|---||||| 181 $ctxt 182 $cc 183 $acr 200 10$aBayesian statistics$b[electronic resource] $ean introduction /$fPeter M. Lee 205 $a4th ed. 210 $aChichester, West Sussex ;$aHoboken, N.J. $d2012 215 $a1 online resource (488 p.) 300 $aDescription based upon print version of record. 311 $a1-118-35977-1 311 $a1-118-33257-1 320 $aIncludes bibliographical references and index. 327 $aBayesian Statistics; Contents; Preface; Preface to the First Edition; 1 Preliminaries; 1.1 Probability and Bayes' Theorem; 1.1.1 Notation; 1.1.2 Axioms for probability; 1.1.3 'Unconditional' probability; 1.1.4 Odds; 1.1.5 Independence; 1.1.6 Some simple consequences of the axioms; Bayes' Theorem; 1.2 Examples on Bayes' Theorem; 1.2.1 The Biology of Twins; 1.2.2 A political example; 1.2.3 A warning; 1.3 Random variables; 1.3.1 Discrete random variables; 1.3.2 The binomial distribution; 1.3.3 Continuous random variables; 1.3.4 The normal distribution; 1.3.5 Mixed random variables 327 $a1.4 Several random variables1.4.1 Two discrete random variables; 1.4.2 Two continuous random variables; 1.4.3 Bayes' Theorem for random variables; 1.4.4 Example; 1.4.5 One discrete variable and one continuous variable; 1.4.6 Independent random variables; 1.5 Means and variances; 1.5.1 Expectations; 1.5.2 The expectation of a sum and of a product; 1.5.3 Variance, precision and standard deviation; 1.5.4 Examples; 1.5.5 Variance of a sum; covariance and correlation; 1.5.6 Approximations to the mean and variance of a function of a random variable; 1.5.7 Conditional expectations and variances 327 $a1.5.8 Medians and modes1.6 Exercises on Chapter 1; 2 Bayesian inference for the normal distribution; 2.1 Nature of Bayesian inference; 2.1.1 Preliminary remarks; 2.1.2 Post is prior times likelihood; 2.1.3 Likelihood can be multiplied by any constant; 2.1.4 Sequential use of Bayes' Theorem; 2.1.5 The predictive distribution; 2.1.6 A warning; 2.2 Normal prior and likelihood; 2.2.1 Posterior from a normal prior and likelihood; 2.2.2 Example; 2.2.3 Predictive distribution; 2.2.4 The nature of the assumptions made; 2.3 Several normal observations with a normal prior; 2.3.1 Posterior distribution 327 $a2.3.2 Example2.3.3 Predictive distribution; 2.3.4 Robustness; 2.4 Dominant likelihoods; 2.4.1 Improper priors; 2.4.2 Approximation of proper priors by improper priors; 2.5 Locally uniform priors; 2.5.1 Bayes' postulate; 2.5.2 Data translated likelihoods; 2.5.3 Transformation of unknown parameters; 2.6 Highest density regions; 2.6.1 Need for summaries of posterior information; 2.6.2 Relation to classical statistics; 2.7 Normal variance; 2.7.1 A suitable prior for the normal variance; 2.7.2 Reference prior for the normal variance; 2.8 HDRs for the normal variance 327 $a2.8.1 What distribution should we be considering?2.8.2 Example; 2.9 The role of sufficiency; 2.9.1 Definition of sufficiency; 2.9.2 Neyman's factorization theorem; 2.9.3 Sufficiency principle; 2.9.4 Examples; 2.9.5 Order statistics and minimal sufficient statistics; 2.9.6 Examples on minimal sufficiency; 2.10 Conjugate prior distributions; 2.10.1 Definition and difficulties; 2.10.2 Examples; 2.10.3 Mixtures of conjugate densities; 2.10.4 Is your prior really conjugate?; 2.11 The exponential family; 2.11.1 Definition; 2.11.2 Examples; 2.11.3 Conjugate densities 327 $a2.11.4 Two-parameter exponential family 330 $aBayesian Statistics is the school of thought that combines prior beliefs with the likelihood of a hypothesis to arrive at posterior beliefs. The first edition of Peter Lee's book appeared in 1989, but the subject has moved ever onwards, with increasing emphasis on Monte Carlo based techniques. This new fourth edition looks at recent techniques such as variational methods, Bayesian importance sampling, approximate Bayesian computation and Reversible Jump Markov Chain Monte Carlo (RJMCMC), providing a concise account of the way in which the Bayesian approach to statistics develo 606 $aBayesian statistical decision theory 606 $aMathematical statistics 608 $aElectronic books. 615 0$aBayesian statistical decision theory. 615 0$aMathematical statistics. 676 $a519.5/42 700 $aLee$b Peter M$0102003 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910462657803321 996 $aBayesian Statistics$9439908 997 $aUNINA LEADER 02095nam 2200397 450 001 9910572183403321 005 20230511182458.0 035 $a(CKB)5860000000047509 035 $a(NjHacI)995860000000047509 035 $a(EXLCZ)995860000000047509 100 $a20230511d2018 uy 0 101 0 $aita 135 $aur||||||||||| 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 03$aLa fortuna del "Secolo d'Oro" $eper Marco Lombardi /$fa cura di Barbara Innocenti 210 1$aFirenze, Italy :$cFirenze University Press,$d2018. 215 $a1 online resource (160 pages) 225 1 $aModerna/comparata 311 $a88-927-3116-5 330 $aIl Seicento e? stato per l'Europa il "secolo del teatro", per la quantita? e qualita? delle opere drammatiche e per la ricchezza delle invenzioni sceniche. Studiarlo, in particolare in area francese e spagnola alla luce della compenetrazione tra Classicismo, Manierismo e Barocco, significa discutere e approfondire alcuni nodi tematici essenziali non solo alla conoscenza di un'epoca storico-letteraria ma alla stessa modernita?. Questo volume, curato da Barbara Innocenti (cui si deve anche la trascrizione di un originale documento sulla morte di Luigi XIV rinvenuto negli archivi pistoiesi), grazie alla partecipazione di noti specialisti che si sono cimentati nella lettura di testi esemplari nel quadro della complessiva storiografi a teatrale e di un ricco apparato iconografico si presenta dunque con il respiro comparatistico necessario. 410 0$aModerna/comparata. 517 $aFortuna del 'Secolo d'Oro' 606 $aFrench drama$y17th century$xHistory and criticism 606 $aFrench drama$y16th century$xHistory and criticism 615 0$aFrench drama$xHistory and criticism. 615 0$aFrench drama$xHistory and criticism. 676 $a842.409 702 $aInnocenti$b Barbara 801 0$bNjHacI 801 1$bNjHacl 906 $aBOOK 912 $a9910572183403321 996 $aLa fortuna del Secolo d'Oro$92057411 997 $aUNINA LEADER 02468nam 2200649 a 450 001 9910953773903321 005 20250919111953.0 010 $a9786613593962 010 $a9780191627897 010 $a0191627895 010 $a9780191627903 010 $a0191627909 010 $a9781280498732 010 $a1280498730 010 $a9780191738449 010 $a0191738441 035 $a(CKB)24235116100041 035 $a(MiAaPQ)EBC3054515 035 $a(MiAaPQ)EBC7036376 035 $a(Au-PeEL)EBL3054515 035 $a(CaPaEBR)ebr10531080 035 $a(CaONFJC)MIL359396 035 $a(OCoLC)922971150 035 $a(PPN)169260232 035 $a(Au-PeEL)EBL7036376 035 $a(OCoLC)785221519 035 $a(EXLCZ)9924235116100041 100 $a20111007d2012 uy 0 101 0 $aeng 135 $aur||||||||||| 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 13$aAn introduction to model-based survey sampling with applications /$fRaymond L. Chambers, Robert G. Clark 205 $a1st ed. 210 $aOxford ;$aNew York $cOxford University Press$d2012 215 $axiv, 265 p. $cill 225 1 $aOxford statistical science series ;$v37 311 08$a9780198566625 320 $aIncludes bibliographical references and index. 327 $apt. 1. Basics of model-based survey inference -- pt. 2. Robust model-based survey methods -- pt. 3. Applications of model-based survey inference. 330 $aThis text brings together important ideas on the model-based approach to sample survey, which has been developed over the last twenty years. Suitable for graduate students and professional statisticians, it moves from basic ideas fundamental to sampling to more rigorous mathematical modelling and data analysis and includes exercises and solutions. 410 0$aOxford statistical science series ;$v37. 606 $aSampling (Statistics)$xMethodology 606 $aSampling (Statistics)$xMathematical models 615 0$aSampling (Statistics)$xMethodology. 615 0$aSampling (Statistics)$xMathematical models. 676 $a519.5/2 700 $aChambers$b R. L$g(Ray L.)$0166179 701 $aClark$b Robert G$0466 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910953773903321 996 $aAn introduction to model-based survey sampling with applications$94359054 997 $aUNINA