04247 am 2200721 n 450 9910315242003321201602092-84788-760-12-84788-696-610.4000/books.enseditions.6987(CKB)3710000000623647(FrMaCLE)OB-enseditions-6987(oapen)https://directory.doabooks.org/handle/20.500.12854/56574(PPN)198369123(EXLCZ)99371000000062364720170110j|||||||| ||| 0freuu||||||m||||txtrdacontentcrdamediacrrdacarrierLa politique au quotidien L’agenda et l’emploi du temps d’une femme politique /Laurent Godmer, Guillaume MarrelLyon ENS Éditions20161 online resource (252 p.) 2-84788-695-8 Que font exactement les femmes et les hommes politiques ? Comment s’organisent leurs activités quotidiennes ? En quoi consiste le travail des dirigeants politiques ? Que savons-nous de l’emploi du temps réel de nos représentants ? Cet ouvrage propose de répondre à ces questions relatives à la vie des démocraties représentatives à partir d’une enquête de sociologie politique inédite menée entre 2010 et 2013. Cette recherche invite à une plongée scientifique au cœur du quotidien de la politique, un univers paradoxalement méconnu. L’exploration porte ici sur l’agenda personnel d’une élue française d’aujourd’hui, une vice-présidente de conseil régional. Le livre décrit ainsi de l’intérieur l’activité politique pour en saisir les mutations contemporaines : la rationalisation et la collectivisation du travail politique, l’importance de l’agenda comme instrument d’organisation et outil de communication, l’emprise croissante des technologies de l’information dans le travail politique, la saturation du temps de travail des élus, mais aussi la construction d’une plus grande disponibilité sur le territoire et d’une certaine transparence. L’enquête révèle enfin comment, en démocratie, les contraintes temporelles des trajectoires politiques personnelles affectent l’action publique, quand l’agenda finit parfois par être utilisé davantage pour préparer les conquêtes électorales futures que pour mettre en œuvre la politique dont l’élu a la charge. Illustration de couverture : © Hélène Robert This book explores politics in a new way. By analysing the organizer and the activity of a contemporary female politician thanks to a study that is both a statistical and ethnographic one, it describes daily politics, a profession which appears to be time-consuming, technical, complex and made of multiple interactions. Following the researches on the sociology of political elites, and on political work that where developed in political science, it brings a new look on a universe of which…LA POLITIQUE AU QUOTIDIEN. L'AGENDA ET L'EMPLOI DU TEMPS D'UNE FEMME POLITIQUE Political Science Public Admin. &amp; DevelopmentSociologypolitiquetravail politiquepolitique localeconseil régionalemploi du tempspoliticspolitical worklocal politicsregional counciltimetablepoliticspolitical worklocal politicsregional counciltimetablePolitical Science Public Admin. &amp; DevelopmentSociologypolitiquetravail politiquepolitique localeconseil régionalemploi du tempspoliticspolitical worklocal politicsregional counciltimetableGodmer Laurent1296026Marrel Guillaume1299945FR-FrMaCLEBOOK9910315242003321La politique au quotidien3025329UNINA05357nam 2200721 a 450 991014125970332120230801223456.01-118-35887-21-280-87995-597866137212661-118-35880-51-118-35888-01-118-35943-7(CKB)2670000000208518(EBL)954614(OCoLC)798536286(SSID)ssj0000676769(PQKBManifestationID)11474980(PQKBTitleCode)TC0000676769(PQKBWorkID)10683649(PQKB)10317098(MiAaPQ)EBC954614(DLC) 2012018371(Au-PeEL)EBL954614(CaPaEBR)ebr10579514(CaONFJC)MIL372126(EXLCZ)99267000000020851820120503d2012 uy 0engurcn|||||||||txtccrBasic and advanced Bayesian structural equation modeling[electronic resource] with applications in the medical and behavioral sciences /Sik-Yum Lee and Xin-Yuan SongHoboken Wiley20121 online resource (397 p.)Wiley series in probability and statisticsDescription based upon print version of record.0-470-66952-7 Includes bibliographical references and index.Basic and Advanced Bayesian Structural Equation Modeling; Contents; About the authors; Preface; 1 Introduction; 1.1 Observed and latent variables; 1.2 Structural equation model; 1.3 Objectives of the book; 1.4 The Bayesian approach; 1.5 Real data sets and notation; Appendix 1.1: Information on real data sets; References; 2 Basic concepts and applications of structural equation models; 2.1 Introduction; 2.2 Linear SEMs; 2.2.1 Measurement equation; 2.2.2 Structural equation and one extension; 2.2.3 Assumptions of linear SEMs; 2.2.4 Model identification; 2.2.5 Path diagram2.3 SEMs with fixed covariates 2.3.1 The model; 2.3.2 An artificial example; 2.4 Nonlinear SEMs; 2.4.1 Basic nonlinear SEMs; 2.4.2 Nonlinear SEMs with fixed covariates; 2.4.3 Remarks; 2.5 Discussion and conclusions; References; 3 Bayesian methods for estimating structural equation models; 3.1 Introduction; 3.2 Basic concepts of the Bayesian estimation and prior distributions; 3.2.1 Prior distributions; 3.2.2 Conjugate prior distributions in Bayesian analyses of SEMs; 3.3 Posterior analysis using Markov chain Monte Carlo methods; 3.4 Application of Markov chain Monte Carlo methods3.5 Bayesian estimation via WinBUGS Appendix 3.1: The gamma, inverted gamma, Wishart, and inverted Wishart distributions and their characteristics; Appendix 3.2: The Metropolis-Hastings algorithm; Appendix 3.3: Conditional distributions [Ω|Y,θ] and [θ|Y,Ω]; Appendix 3.4: Conditional distributions [Ω|Y,θ] and [θ|Y,Ω] in nonlinear SEMs with covariates; Appendix 3.5: WinBUGS code; Appendix 3.6: R2WinBUGS code; References; 4 Bayesian model comparison and model checking; 4.1 Introduction; 4.2 Bayes factor; 4.2.1 Path sampling; 4.2.2 A simulation study; 4.3 Other model comparison statistics4.3.1 Bayesian information criterion and Akaike information criterion 4.3.2 Deviance information criterion; 4.3.3 Lν-measure; 4.4 Illustration; 4.5 Goodness of fit and model checking methods; 4.5.1 Posterior predictive p-value; 4.5.2 Residual analysis; Appendix 4.1: WinBUGS code; Appendix 4.2: R code in Bayes factor example; Appendix 4.3: Posterior predictive p-value for model assessment; References; 5 Practical structural equation models; 5.1 Introduction; 5.2 SEMs with continuous and ordered categorical variables; 5.2.1 Introduction; 5.2.2 The basic model; 5.2.3 Bayesian analysis5.2.4 Application: Bayesian analysis of quality of life data 5.2.5 SEMs with dichotomous variables; 5.3 SEMs with variables from exponential family distributions; 5.3.1 Introduction; 5.3.2 The SEM framework with exponential family distributions; 5.3.3 Bayesian inference; 5.3.4 Simulation study; 5.4 SEMs with missing data; 5.4.1 Introduction; 5.4.2 SEMs with missing data that are MAR; 5.4.3 An illustrative example; 5.4.4 Nonlinear SEMs with nonignorable missing data; 5.4.5 An illustrative real exampleAppendix 5.1: Conditional distributions and implementation of the MH algorithm for SEMs with continuous and ordered categorical variables"This book introduces the Bayesian approach to SEMs, including the selection of prior distributions and data augmentation, and offers an overview of the subject's recent advances"--Provided by publisher.Wiley series in probability and statistics.Structural equation modelingBayesian statistical decision theoryStructural equation modeling.Bayesian statistical decision theory.519.5/3MAT029000bisacshLee Sik-Yum308566Song Xin-Yuan891931MiAaPQMiAaPQMiAaPQBOOK9910141259703321Basic and advanced Bayesian structural equation modeling1992023UNINA02376nam0 2200529 i 450 VAN012500920231002021711.451N978331994893520191029d2018 |0itac50 baengCH|||| |||||Stochastic Evolution SystemsLinear Theory and Applications to Non-Linear FilteringBoris L. Rozovsky, Sergey V. Lototsky2. edChamSpringer2018xvi, 330 p.ill.24 cm001VAN01038262001 Probability theory and stochastic modelling210 Berlin [etc.]Springer1988-89VAN0236511EVOLYUTSIONNYYe STOKHASTICHESKIYe SISTEMY255424260H15Stochastic partial differential equations (aspects of stochastic analysis) [MSC 2020]VANC021488MF35R60PDEs with randomness, stochastic partial differential equations [MSC 2020]VANC025169MFBackward diffusion equationKW:KBoundary Value ProblemsKW:KChaos solution of parabolic equationsKW:KDiffusion ProcessesKW:KExtrapolationKW:KFiltering problemKW:KHormander's condition in filteringKW:KInterpolationKW:KLocal martingaleKW:KMarkov propertyKW:KMartingalesKW:KPartial differential equationsKW:KSobolev spacesKW:KStochastic characteristicsKW:KStochastic integration in Hilbert spacesKW:KCHChamVANL001889RozovskyBoris L.VANV094997767827LototskySergey V.VANV076102767168Springer <editore>VANV108073650ITSOL20240614RICAhttp://doi.org/10.1007/978-3-319-94893-5E-book – Accesso al full-text attraverso riconoscimento IP di Ateneo, proxy e/o ShibbolethBIBLIOTECA DEL DIPARTIMENTO DI MATEMATICA E FISICAIT-CE0120VAN08NVAN0125009BIBLIOTECA DEL DIPARTIMENTO DI MATEMATICA E FISICA08CONS e-book 1385 08eMF1385 20191029 EVOLYUTSIONNYYe STOKHASTICHESKIYe SISTEMY2554242UNICAMPANIA00652ogm 2200169z- 450 9910320324803321(CKB)4910000000113549(EXLCZ)99491000000011354920240702cuuuuuuuu -u- vengHydroponics: A Versatile System to Study Nutrient Allocation and Plant Responses to Nutrient Availability and Exposure to Toxic ElementsMyJoVE CorpHydroponicsVIDEO9910320324803321Hydroponics: A Versatile System to Study Nutrient Allocation and Plant Responses to Nutrient Availability and Exposure to Toxic Elements2218260UNINA