LEADER 03139nam 2200577 a 450 001 9910782503703321 005 20230617041206.0 010 $a1-281-22449-9 010 $a9786611224493 010 $a81-224-2335-3 035 $a(CKB)1000000000689404 035 $a(EBL)333151 035 $a(OCoLC)309761736 035 $a(SSID)ssj0000672849 035 $a(PQKBManifestationID)11424671 035 $a(PQKBTitleCode)TC0000672849 035 $a(PQKBWorkID)10636603 035 $a(PQKB)10109049 035 $a(MiAaPQ)EBC333151 035 $a(Au-PeEL)EBL333151 035 $a(CaPaEBR)ebr10323382 035 $a(CaONFJC)MIL122449 035 $a(EXLCZ)991000000000689404 100 $a20091007d2005 uy 0 101 0 $aeng 135 $aur|n|---||||| 181 $ctxt 182 $cc 183 $acr 200 10$aImmunology$b[electronic resource] $eintroductory textbook /$fNandini Shetty 205 $aRev. 2nd ed. 210 $aNew Delhi $cNew Age International (P) Ltd., Publishers$d2005 215 $a1 online resource (224 p.) 300 $aIncludes index. 311 $a81-224-1678-0 327 $aCover; Preface to the Second Edition; Preface to the First Edition; Contents; Chapter 1. Milestones in Immunology; Chapter 2. Innate Immunity; Chapter 3. Immunobiology; Chapter 4. Antigens and Immunogenicity; Chapter 5. Immunoglobulins I: Structure and Function; Chapter 6. Immunoglobulins II: The Genetics of Antibody Diversity; Chapter 7. The Complement System; Chapter 8. Detection and Application of Antigen-Antibody Reactions; Chapter 9. Monoclonal Antibodies; Chapter 10. The Major Histocompatibility Complex; Chapter 11. Immune Response Mechanisms I: B And T Lymphocytes 327 $aChapter 12. Immune Response Mechanisms II: Antigen Presentation and Processing Mechanisms of Lymphocyte Activation; Chapter 13. Cytokines; Chapter 14. Cell-Mediated Immunity; Chapter 15. Hypersensitivity; Chapter 16. Immunologic Tolerance and Autoimmunity; Chapter 17. Immunopotentiation and Immunosuppression; Chapter 18. Transplantation Immunology; Chapter 19. Tumour Immunology; Chapter 20. Immunity Against Infectious Diseases; Chapter 21. Immunization; Chapter 22. Immunodeficiency Diseases; Chapter 23. Immunology of HIV Infection; Chapter 24. Immunity and Malnutrition; Index 330 $aIn this text, the author has tried to unfold before the student, the essence of immunology from its historic beginnings, to the understanding of basic concepts of host defence and onto the many clinical applications of this science. Immunology: Introductory Textbook, covers the topics of Immunobiology, Immunoglobulins, Immuno-response Mechanisms, Transplantation Immunology and Immunodeficiency Diseases, Immunopotentiation and Immuno-suppression, among others. 606 $aImmunology 615 0$aImmunology. 676 $a571.96 676 $a616.07 676 $a616.079 700 $aShetty$b N$g(Nandini)$01529086 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910782503703321 996 $aImmunology$93773118 997 $aUNINA LEADER 05884nam 2200817Ia 450 001 9910819512103321 005 20200520144314.0 010 $a9781118394472 010 $a111839447X 010 $a9781283656344 010 $a1283656345 010 $a9781118394496 010 $a1118394496 035 $a(CKB)2670000000261636 035 $a(EBL)1037158 035 $a(SSID)ssj0000757653 035 $a(PQKBManifestationID)11463479 035 $a(PQKBTitleCode)TC0000757653 035 $a(PQKBWorkID)10771376 035 $a(PQKB)11752191 035 $a(DLC) 2012034365 035 $a(Au-PeEL)EBL1037158 035 $a(CaPaEBR)ebr10608638 035 $a(CaONFJC)MIL396884 035 $a(CaSebORM)9781118394328 035 $a(MiAaPQ)EBC1037158 035 $a(OCoLC)813535628 035 $a(OCoLC)828687957 035 $a(OCoLC)ocn828687957 035 $a(Perlego)1002791 035 $a(EXLCZ)992670000000261636 100 $a20120815d2012 uy 0 101 0 $aeng 135 $aur|n|---||||| 181 $ctxt 182 $cc 183 $acr 200 00$aCase studies in Bayesian statistical modelling and analysis /$fedited by Clair Alston, Kerrie Mengersen, and Anthony Pettitt 205 $a1st edition 210 $aChichester, West Sussex $cJohn Wiley & Sons Inc.$d2012 215 $a1 online resource (499 p.) 225 1 $aWiley Series in Probability and Statistics 300 $aDescription based upon print version of record. 311 08$a9781118394328 311 08$a1118394321 311 08$a9781119941828 311 08$a1119941822 320 $aIncludes bibliographical references and index. 327 $aCase Studies in Bayesian Statistical Modelling and Analysis; Contents; Preface; List of contributors; 1 Introduction; 1.1 Introduction; 1.2 Overview; 1.3 Further reading; 1.3.1 Bayesian theory and methodology; 1.3.2 Bayesian methodology; 1.3.3 Bayesian computation; 1.3.4 Bayesian software; 1.3.5 Applications; References; 2 Introduction to MCMC; 2.1 Introduction; 2.2 Gibbs sampling; 2.2.1 Example: Bivariate normal; 2.2.2 Example: Change-point model; 2.3 Metropolis-Hastings algorithms; 2.3.1 Example: Component-wise MH or MH within Gibbs; 2.3.2 Extensions to basic MCMC; 2.3.3 Adaptive MCMC 327 $a2.3.4 Doubly intractable problems2.4 Approximate Bayesian computation; 2.5 Reversible jump MCMC; 2.6 MCMC for some further applications; References; 3 Priors: Silent or active partners of Bayesian inference?; 3.1 Priors in the very beginning; 3.1.1 Priors as a basis for learning; 3.1.2 Priors and philosophy; 3.1.3 Prior chronology; 3.1.4 Pooling prior information; 3.2 Methodology I: Priors defined by mathematical criteria; 3.2.1 Conjugate priors; 3.2.2 Impropriety and hierarchical priors; 3.2.3 Zellner's g-prior for regression models; 3.2.4 Objective priors 327 $a3.3 Methodology II: Modelling informative priors3.3.1 Informative modelling approaches; 3.3.2 Elicitation of distributions; 3.4 Case studies; 3.4.1 Normal likelihood: Time to submit research dissertations; 3.4.2 Binomial likelihood: Surveillance for exotic plant pests; 3.4.3 Mixture model likelihood: Bioregionalization; 3.4.4 Logistic regression likelihood: Mapping species distribution via habitat models; 3.5 Discussion; 3.5.1 Limitations; 3.5.2 Finding out about the problem; 3.5.3 Prior formulation; 3.5.4 Communication; 3.5.5 Conclusion; Acknowledgements; References 327 $a4 Bayesian analysis of the normal linear regression model4.1 Introduction; 4.2 Case studies; 4.2.1 Case study 1: Boston housing data set; 4.2.2 Case study 2: Production of cars and station wagons; 4.3 Matrix notation and the likelihood; 4.4 Posterior inference; 4.4.1 Natural conjugate prior; 4.4.2 Alternative prior specifications; 4.4.3 Generalizations of the normal linear model; 4.4.4 Variable selection; 4.5 Analysis; 4.5.1 Case study 1: Boston housing data set; 4.5.2 Case study 2: Car production data set; References; 5 Adapting ICU mortality models for local data: A Bayesian approach 327 $a5.1 Introduction5.2 Case study: Updating a known risk-adjustment model for local use; 5.3 Models and methods; 5.4 Data analysis and results; 5.4.1 Updating using the training data; 5.4.2 Updating the model yearly; 5.5 Discussion; References; 6 A Bayesian regression model with variable selection for genome-wide association studies; 6.1 Introduction; 6.2 Case study: Case-control of Type 1 diabetes; 6.3 Case study: GENICA; 6.4 Models and methods; 6.4.1 Main effect models; 6.4.2 Main effects and interactions; 6.5 Data analysis and results; 6.5.1 WTCCC TID; 6.5.2 GENICA; 6.6 Discussion 327 $aAcknowledgements 330 $a Provides an accessible foundation to Bayesian analysis using real world models This book aims to present an introduction to Bayesian modelling and computation, by considering real case studies drawn from diverse fields spanning ecology, health, genetics and finance. Each chapter comprises a description of the problem, the corresponding model, the computational method, results and inferences as well as the issues that arise in the implementation of these approaches. Case Studies in Bayesian Statistical Modelling and Analysis: Illustrates how 410 0$aWiley Series in Probability and Statistics 606 $aBayesian statistical decision theory 606 $aStatistical decision 615 0$aBayesian statistical decision theory. 615 0$aStatistical decision. 676 $a519.5/42 701 $aAlston$b Clair$01661161 701 $aMengersen$b Kerrie L$01654858 701 $aPettitt$b Anthony$g(Anthony N.)$01661162 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910819512103321 996 $aCase studies in Bayesian statistical modelling and analysis$94016920 997 $aUNINA