01064nam--2200361---450-99000174157020331620051026092009.0000174157USA01000174157(ALEPH)000174157USA0100017415720040609d1952----km-y0itay0103----baitaITa|||||||001yyLineamenti di caratterologia e tipologia applicate all'educazioneGiacomo LorenziniTorinoSEI1952XIV, 426 p., [7] p. di tav.ill.22 cm20012001001-------2001Carattere155.2LORENZINI,Giacomo64256ITsalbcISBD990001741570203316II.4. 3229(VI Ps B 105)2261 L.M.VI Ps BBKUMASIAV41020040609USA011925COPAT59020051026USA010920Lineamenti di caratterologia e tipologia applicate all'educazione946464UNISA02243oam 2200517I 450 991046573380332120200520144314.01-315-53915-21-134-94754-210.4324/9781315539157 (CKB)3710000000648415(EBL)4511805(MiAaPQ)EBC4511805(Au-PeEL)EBL4511805(CaPaEBR)ebr11203969(CaONFJC)MIL916800(OCoLC)948924945(OCoLC)947837756(EXLCZ)99371000000064841520130331d2005 uy 0engur|n|---|||||rdacontentrdamediardacarrierTeaching dance studies /edited by Judith Chazin-BennahumNew York :Routledge,2005.1 online resource (269 p.)Description based upon print version of record.0-415-97036-9 0-415-97035-0 Includes bibliographical references at the end of each chapters and index.Cover; Half Title; Title; Copyright; Contents; Acknowledgments; Introduction; 1 Teaching Movement Analysis; 2 Dance Theory?; 3 From Improvisation to Choreography: The Critical Bridge; 4 Wild Speculations and Simple Thoughts: Teaching Music to Dancers; 5 Teaching Dance on Film and Film Dance; 6 Teaching Dance History: A Querying Stance as Millennial Lens; 7 On Teaching Dance Criticism; 8 The Anthropology of Dance: Textural, Theoretical, and Experiential Ways of Knowing; 9 Standing Aside and Making Space: Mentoring Student Choreographers; 10 Kinesiology and Injury Prevention; 11 Labanotation12 Documentation, Preservation, and Access: Ensuring a Future for Dance's Legacy13 Reflections on Educating Dance Educators; Contributors; IndexDanceStudy and teaching (Higher)Electronic books.DanceStudy and teaching (Higher)792.8/071/1Chazin-Bennahum Judith945626MiAaPQMiAaPQMiAaPQBOOK9910465733803321Teaching dance studies2135407UNINA05645nam 2200757 a 450 991096192050332120220623161957.097866132728299781283272827128327282297801238702160123870216(CKB)2670000000122785(EBL)767268(OCoLC)760173084(SSID)ssj0000534331(PQKBManifestationID)12231932(PQKBTitleCode)TC0000534331(PQKBWorkID)10511445(PQKB)10942182(Au-PeEL)EBL767268(CaPaEBR)ebr10503269(CaONFJC)MIL327282(PPN)170603989(FR-PaCSA)88812232(MiAaPQ)EBC767268(FRCYB88812232)88812232(EXLCZ)99267000000012278520110820d2012 uy 0engurcn|||||||||txtccrBayesian population analysis using WinBUGS a hierarchical perspective /Marc Kéry and Michael Schaub ; foreword by Steven R. Beissinger1st ed.Boston Academic Press20121 online resource (555 p.)Description based upon print version of record.9780123870209 0123870208 Includes bibliographical references and index.Front Cover; Bayesian Population Analysis using WinBUGS: A Hierarchical Perspective; Copyright; Dedication; Table of Contents; Foreword; Preface; Acknowledgments; 1 Introduction; 1.1 Ecology: The Study of Distribution and Abundance and of the Mechanisms Driving Their Change; 1.2 Genesis of Ecological Observations; 1.3 The Binomial Distribution as a Canonical Description of the Observation Process; 1.4 Structure and Overview of the Contents of this Book; 1.5 Benefits of Analyzing Simulated Data Sets: An Example of Bias and Precision; 1.6 Summary and Outlook; 1.7 Exercises2 Brief Introduction to Bayesian Statistical Modeling 2.1 Introduction; 2.2 Role of Models in Science; 2.3 Statistical Models; 2.4 Frequentist and Bayesian Analysis of Statistical Models; 2.5 Bayesian Computation; 2.6 WinBUGS; 2.7 Advantages and Disadvantages of Bayesian Analyses by Posterior Sampling; 2.8 Hierarchical Models; 2.9 Summary and Outlook; 3 Introduction to the Generalized Linear Model: The Simplest Model for Count Data; 3.1 Introduction; 3.2 Statistical Models: Response = Signal + Noise; 3.2.1 The Noise Component; 3.2.2 The Signal Component3.2.3 Bringing the Noise and the Signal Components Together: The Link Function 3.3 Poisson GLM in R and WinBUGS for Modeling Time Series of Counts; 3.3.1 Generation and Analysis of Simulated Data; 3.3.2 Analysis of Real Data Set; 3.4 Poisson GLM for Modeling Fecundity; 3.5 Binomial GLM for Modeling Bounded Counts or Proportions; 3.5.1 Generation and Analysis of Simulated Data; 3.5.2 Analysis of Real Data Set; 3.6 Summary and Outlook; 3.7 Exercises; 4 Introduction to Random Effects: Conventional Poisson GLMM for Count Data; 4.1 Introduction; 4.1.1 An Example; 4.1.2 What Are Random Effects?4.1.3 Why Do We Treat Batches of Effects as Random?Scope of Inference; Assessment of Variability; Partitioning of Variability; Modeling of Correlations among Parameters; Accounting for All Random Processes in a Modeled System; Avoiding Pseudoreplication; Borrowing Strength; Random Effects as a Compromise between Pooling and No Pooling of Batched Effects; Combining Information; 4.1.4 Why Should We Ever Treat a Factor as Fixed?; 4.2 Accounting for Overdispersion by Random Effects-Modeling in R and WinBUGS; 4.2.1 Generation and Analysis of Simulated Data; 4.2.2 Analysis of Real Data4.3 Mixed Models with Random Effects for Variability among Groups (Site and Year Effects)4.3.1 Generation and Analysis of Simulated Data; 4.3.2 Analysis of Real Data Set; Null or Intercept-Only Model; Fixed Site Effects; Fixed Site and Fixed Year Effects; Random Site Effects (No Year Effects); Random Site and Random Year Effects; Random Site and Random Year Effects and First-Year Fixed Observer Effect; Random Site and Random Year Effects, First-Year Fixed Observer Effect, and Overall Linear Time Trend; The Full Model; 4.4 Summary and Outlook; 4.5 Exercises5 State-Space Models for Population CountsBayesian statistics has exploded into biology and its sub-disciplines, such as ecology, over the past decade. The free software program WinBUGS, and its open-source sister OpenBugs, is currently the only flexible and general-purpose program available with which the average ecologist can conduct standard and non-standard Bayesian statistics. Comprehensive and richly commented examples illustrate a wide range of models that are most relevant to the research of a modern population ecologist All WinBUGS/OpenBUGS analyses are completely integrated in software R<liPopulation biologyData processingR (Computer program language)Population biologyData processing.R (Computer program language)577.880285577.880727577.880285Kéry Marc1798642Schaub Michael1798643Beissinger Steven R1798644MiAaPQMiAaPQMiAaPQBOOK9910961920503321Bayesian population analysis using WinBUGS4341518UNINA