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Applied Bayesian modeling and causal inference from incomplete-data perspectives [[electronic resource] ] : an essential journey with Donald Rubin's statistical family / / edited by Andrew Gelman, Xiao-Li Meng
| Applied Bayesian modeling and causal inference from incomplete-data perspectives [[electronic resource] ] : an essential journey with Donald Rubin's statistical family / / edited by Andrew Gelman, Xiao-Li Meng |
| Edizione | [1st ed.] |
| Pubbl/distr/stampa | Chichester, West Sussex, England ; ; Hoboken, NJ, : Wiley, c2004 |
| Descrizione fisica | 1 online resource (437 p.) |
| Disciplina |
519.5/42
519.542 |
| Altri autori (Persone) |
RubinDonald B
GelmanAndrew MengXiao-Li |
| Collana | Wiley series in probability and statistics |
| Soggetto topico |
Bayesian statistical decision theory
Missing observations (Statistics) |
| ISBN |
1-280-26898-0
9786610268986 0-470-09045-6 0-470-09044-8 |
| Formato | Materiale a stampa  |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Applied Bayesian Modeling and Causal Inference from Incomplete-Data Perspectives; Contents; Preface; I Casual inference and observational studies; 1 An overview of methods for causal inference from observational studies; 1.1 Introduction; 1.2 Approaches based on causal models; 1.3 Canonical inference; 1.4 Methodologic modeling; 1.5 Conclusion; 2 Matching in observational studies; 2.1 The role of matching in observational studies; 2.2 Why match?; 2.3 Two key issues: balance and structure; 2.4 Additional issues; 3 Estimating causal effects in nonexperimental studies; 3.1 Introduction
3.2 Identifying and estimating the average treatment effect3.3 The NSW data; 3.4 Propensity score estimates; 3.5 Conclusions; 4 Medication cost sharing and drug spending in Medicare; 4.1 Methods; 4.2 Results; 4.3 Study limitations; 4.4 Conclusions and policy implications; 5 A comparison of experimental and observational data analyses; 5.1 Experimental sample; 5.2 Constructed observational study; 5.3 Concluding remarks; 6 Fixing broken experiments using the propensity score; 6.1 Introduction; 6.2 The lottery data; 6.3 Estimating the propensity scores; 6.4 Results; 6.5 Concluding remarks 7 The propensity score with continuous treatments7.1 Introduction; 7.2 The basic framework; 7.3 Bias removal using the GPS; 7.4 Estimation and inference; 7.5 Application: the Imbens-Rubin-Sacerdote lottery sample; 7.6 Conclusion; 8 Causal inference with instrumental variables; 8.1 Introduction; 8.2 Key assumptions for the LATE interpretation of the IV estimand; 8.3 Estimating causal effects with IV; 8.4 Some recent applications; 8.5 Discussion; 9 Principal stratification; 9.1 Introduction: partially controlled studies; 9.2 Examples of partially controlled studies; 9.3 Principal stratification 9.4 Estimands9.5 Assumptions; 9.6 Designs and polydesigns; II Missing data modeling; 10 Nonresponse adjustment in government statistical agencies: constraints, inferential goals, and robustness issues; 10.1 Introduction: a wide spectrum of nonresponse adjustment efforts in government statistical agencies; 10.2 Constraints; 10.3 Complex estimand structures, inferential goals, and utility functions; 10.4 Robustness; 10.5 Closing remarks; 11 Bridging across changes in classification systems; 11.1 Introduction; 11.2 Multiple imputation to achieve comparability of industry and occupation codes 11.3 Bridging the transition from single-race reporting to multiple-race reporting11.4 Conclusion; 12 Representing the Census undercount by multiple imputation of households; 12.1 Introduction; 12.2 Models; 12.3 Inference; 12.4 Simulation evaluations; 12.5 Conclusion; 13 Statistical disclosure techniques based on multiple imputation; 13.1 Introduction; 13.2 Full synthesis; 13.3 SMIKe and MIKe; 13.4 Analysis of synthetic samples; 13.5 An application; 13.6 Conclusions; 14 Designs producing balanced missing data: examples from the National Assessment of Educational Progress; 14.1 Introduction 14.2 Statistical methods in NAEP |
| Record Nr. | UNINA-9910144711403321 |
| Chichester, West Sussex, England ; ; Hoboken, NJ, : Wiley, c2004 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Applied Bayesian modeling and causal inference from incomplete-data perspectives [[electronic resource] ] : an essential journey with Donald Rubin's statistical family / / edited by Andrew Gelman, Xiao-Li Meng
| Applied Bayesian modeling and causal inference from incomplete-data perspectives [[electronic resource] ] : an essential journey with Donald Rubin's statistical family / / edited by Andrew Gelman, Xiao-Li Meng |
| Edizione | [1st ed.] |
| Pubbl/distr/stampa | Chichester, West Sussex, England ; ; Hoboken, NJ, : Wiley, c2004 |
| Descrizione fisica | 1 online resource (437 p.) |
| Disciplina |
519.5/42
519.542 |
| Altri autori (Persone) |
RubinDonald B
GelmanAndrew MengXiao-Li |
| Collana | Wiley series in probability and statistics |
| Soggetto topico |
Bayesian statistical decision theory
Missing observations (Statistics) |
| ISBN |
1-280-26898-0
9786610268986 0-470-09045-6 0-470-09044-8 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Applied Bayesian Modeling and Causal Inference from Incomplete-Data Perspectives; Contents; Preface; I Casual inference and observational studies; 1 An overview of methods for causal inference from observational studies; 1.1 Introduction; 1.2 Approaches based on causal models; 1.3 Canonical inference; 1.4 Methodologic modeling; 1.5 Conclusion; 2 Matching in observational studies; 2.1 The role of matching in observational studies; 2.2 Why match?; 2.3 Two key issues: balance and structure; 2.4 Additional issues; 3 Estimating causal effects in nonexperimental studies; 3.1 Introduction
3.2 Identifying and estimating the average treatment effect3.3 The NSW data; 3.4 Propensity score estimates; 3.5 Conclusions; 4 Medication cost sharing and drug spending in Medicare; 4.1 Methods; 4.2 Results; 4.3 Study limitations; 4.4 Conclusions and policy implications; 5 A comparison of experimental and observational data analyses; 5.1 Experimental sample; 5.2 Constructed observational study; 5.3 Concluding remarks; 6 Fixing broken experiments using the propensity score; 6.1 Introduction; 6.2 The lottery data; 6.3 Estimating the propensity scores; 6.4 Results; 6.5 Concluding remarks 7 The propensity score with continuous treatments7.1 Introduction; 7.2 The basic framework; 7.3 Bias removal using the GPS; 7.4 Estimation and inference; 7.5 Application: the Imbens-Rubin-Sacerdote lottery sample; 7.6 Conclusion; 8 Causal inference with instrumental variables; 8.1 Introduction; 8.2 Key assumptions for the LATE interpretation of the IV estimand; 8.3 Estimating causal effects with IV; 8.4 Some recent applications; 8.5 Discussion; 9 Principal stratification; 9.1 Introduction: partially controlled studies; 9.2 Examples of partially controlled studies; 9.3 Principal stratification 9.4 Estimands9.5 Assumptions; 9.6 Designs and polydesigns; II Missing data modeling; 10 Nonresponse adjustment in government statistical agencies: constraints, inferential goals, and robustness issues; 10.1 Introduction: a wide spectrum of nonresponse adjustment efforts in government statistical agencies; 10.2 Constraints; 10.3 Complex estimand structures, inferential goals, and utility functions; 10.4 Robustness; 10.5 Closing remarks; 11 Bridging across changes in classification systems; 11.1 Introduction; 11.2 Multiple imputation to achieve comparability of industry and occupation codes 11.3 Bridging the transition from single-race reporting to multiple-race reporting11.4 Conclusion; 12 Representing the Census undercount by multiple imputation of households; 12.1 Introduction; 12.2 Models; 12.3 Inference; 12.4 Simulation evaluations; 12.5 Conclusion; 13 Statistical disclosure techniques based on multiple imputation; 13.1 Introduction; 13.2 Full synthesis; 13.3 SMIKe and MIKe; 13.4 Analysis of synthetic samples; 13.5 An application; 13.6 Conclusions; 14 Designs producing balanced missing data: examples from the National Assessment of Educational Progress; 14.1 Introduction 14.2 Statistical methods in NAEP |
| Record Nr. | UNINA-9910829865903321 |
| Chichester, West Sussex, England ; ; Hoboken, NJ, : Wiley, c2004 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Applied Bayesian modeling and causal inference from incomplete-data perspectives : an essential journey with Donald Rubin's statistical family / / edited by Andrew Gelman, Xiao-Li Meng
| Applied Bayesian modeling and causal inference from incomplete-data perspectives : an essential journey with Donald Rubin's statistical family / / edited by Andrew Gelman, Xiao-Li Meng |
| Edizione | [1st ed.] |
| Pubbl/distr/stampa | Chichester, West Sussex, England ; ; Hoboken, NJ, : Wiley, c2004 |
| Descrizione fisica | 1 online resource (437 p.) |
| Disciplina | 519.5/42 |
| Altri autori (Persone) |
RubinDonald B
GelmanAndrew MengXiao-Li |
| Collana | Wiley series in probability and statistics |
| Soggetto topico |
Bayesian statistical decision theory
Missing observations (Statistics) |
| ISBN |
1-280-26898-0
9786610268986 0-470-09045-6 0-470-09044-8 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Applied Bayesian Modeling and Causal Inference from Incomplete-Data Perspectives; Contents; Preface; I Casual inference and observational studies; 1 An overview of methods for causal inference from observational studies; 1.1 Introduction; 1.2 Approaches based on causal models; 1.3 Canonical inference; 1.4 Methodologic modeling; 1.5 Conclusion; 2 Matching in observational studies; 2.1 The role of matching in observational studies; 2.2 Why match?; 2.3 Two key issues: balance and structure; 2.4 Additional issues; 3 Estimating causal effects in nonexperimental studies; 3.1 Introduction
3.2 Identifying and estimating the average treatment effect3.3 The NSW data; 3.4 Propensity score estimates; 3.5 Conclusions; 4 Medication cost sharing and drug spending in Medicare; 4.1 Methods; 4.2 Results; 4.3 Study limitations; 4.4 Conclusions and policy implications; 5 A comparison of experimental and observational data analyses; 5.1 Experimental sample; 5.2 Constructed observational study; 5.3 Concluding remarks; 6 Fixing broken experiments using the propensity score; 6.1 Introduction; 6.2 The lottery data; 6.3 Estimating the propensity scores; 6.4 Results; 6.5 Concluding remarks 7 The propensity score with continuous treatments7.1 Introduction; 7.2 The basic framework; 7.3 Bias removal using the GPS; 7.4 Estimation and inference; 7.5 Application: the Imbens-Rubin-Sacerdote lottery sample; 7.6 Conclusion; 8 Causal inference with instrumental variables; 8.1 Introduction; 8.2 Key assumptions for the LATE interpretation of the IV estimand; 8.3 Estimating causal effects with IV; 8.4 Some recent applications; 8.5 Discussion; 9 Principal stratification; 9.1 Introduction: partially controlled studies; 9.2 Examples of partially controlled studies; 9.3 Principal stratification 9.4 Estimands9.5 Assumptions; 9.6 Designs and polydesigns; II Missing data modeling; 10 Nonresponse adjustment in government statistical agencies: constraints, inferential goals, and robustness issues; 10.1 Introduction: a wide spectrum of nonresponse adjustment efforts in government statistical agencies; 10.2 Constraints; 10.3 Complex estimand structures, inferential goals, and utility functions; 10.4 Robustness; 10.5 Closing remarks; 11 Bridging across changes in classification systems; 11.1 Introduction; 11.2 Multiple imputation to achieve comparability of industry and occupation codes 11.3 Bridging the transition from single-race reporting to multiple-race reporting11.4 Conclusion; 12 Representing the Census undercount by multiple imputation of households; 12.1 Introduction; 12.2 Models; 12.3 Inference; 12.4 Simulation evaluations; 12.5 Conclusion; 13 Statistical disclosure techniques based on multiple imputation; 13.1 Introduction; 13.2 Full synthesis; 13.3 SMIKe and MIKe; 13.4 Analysis of synthetic samples; 13.5 An application; 13.6 Conclusions; 14 Designs producing balanced missing data: examples from the National Assessment of Educational Progress; 14.1 Introduction 14.2 Statistical methods in NAEP |
| Record Nr. | UNINA-9910876584003321 |
| Chichester, West Sussex, England ; ; Hoboken, NJ, : Wiley, c2004 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Applied Bayesian modelling / / Peter Congdon
| Applied Bayesian modelling / / Peter Congdon |
| Autore | Congdon P. |
| Edizione | [Second edition.] |
| Pubbl/distr/stampa | Chichester, [England] : , : Wiley, , 2014 |
| Descrizione fisica | 1 online resource (465 p.) |
| Disciplina | 519.5/42 |
| Collana | Wiley Series in Probability and Statistics |
| Soggetto topico |
Bayesian statistical decision theory
Mathematical statistics |
| ISBN |
1-118-89504-5
1-118-89505-3 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Cover; Title Page; Copyright; Contents; Preface; Chapter 1 Bayesian methods and Bayesian estimation; 1.1 Introduction; 1.1.1 Summarising existing knowledge: Prior densities for parameters; 1.1.2 Updating information: Prior, likelihood and posterior densities; 1.1.3 Predictions and assessment; 1.1.4 Sampling parameters; 1.2 MCMC techniques: The Metropolis-Hastings algorithm; 1.2.1 Gibbs sampling; 1.2.2 Other MCMC algorithms; 1.2.3 INLA approximations; 1.3 Software for MCMC: BUGS, JAGS and R-INLA; 1.4 Monitoring MCMC chains and assessing convergence; 1.4.1 Convergence diagnostics
1.4.2 Model identifiability1.5 Model assessment; 1.5.1 Sensitivity to priors; 1.5.2 Model checks; 1.5.3 Model choice; References; Chapter 2 Hierarchical models for related units; 2.1 Introduction: Smoothing to the hyper population; 2.2 Approaches to model assessment: Penalised fit criteria, marginal likelihood and predictive methods; 2.2.1 Penalised fit criteria; 2.2.2 Formal model selection using marginal likelihoods; 2.2.3 Estimating model probabilities or marginal likelihoods in practice; 2.2.4 Approximating the posterior density; 2.2.5 Model averaging from MCMC samples 2.2.6 Predictive criteria for model checking and selection: Cross-validation2.2.7 Predictive checks and model choice using complete data replicate sampling; 2.3 Ensemble estimates: Poisson-gamma and Beta-binomial hierarchical models; 2.3.1 Hierarchical mixtures for poisson and binomial data; 2.4 Hierarchical smoothing methods for continuous data; 2.4.1 Priors on hyperparameters; 2.4.2 Relaxing normality assumptions; 2.4.3 Multivariate borrowing of strength; 2.5 Discrete mixtures and dirichlet processes; 2.5.1 Finite mixture models; 2.5.2 Dirichlet process priors 2.6 General additive and histogram smoothing priors2.6.1 Smoothness priors; 2.6.2 Histogram smoothing; Exercises; Notes; References; Chapter 3 Regression techniques; 3.1 Introduction: Bayesian regression; 3.2 Normal linear regression; 3.2.1 Linear regression model checking; 3.3 Simple generalized linear models: Binomial, binary and Poisson regression; 3.3.1 Binary and binomial regression; 3.3.2 Poisson regression; 3.4 Augmented data regression; 3.5 Predictor subset choice; 3.5.1 The g-prior approach; 3.5.2 Hierarchical lasso prior methods; 3.6 Multinomial, nested and ordinal regression 3.6.1 Nested logit specification3.6.2 Ordinal outcomes; Exercises; Notes; References; Chapter 4 More advanced regression techniques; 4.1 Introduction; 4.2 Departures from linear model assumptions and robust alternatives; 4.3 Regression for overdispersed discrete outcomes; 4.3.1 Excess zeroes; 4.4 Link selection; 4.5 Discrete mixture regressions for regression and outlier status; 4.5.1 Outlier accommodation; 4.6 Modelling non-linear regression effects; 4.6.1 Smoothness priors for non-linear regression; 4.6.2 Spline regression and other basis functions; 4.6.3 Priors on basis coefficients 4.7 Quantile regression |
| Record Nr. | UNINA-9910132210503321 |
Congdon P.
|
||
| Chichester, [England] : , : Wiley, , 2014 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Approximation methods for efficient learning of Bayesian networks [[electronic resource] /] / Carsten Riggelsen
| Approximation methods for efficient learning of Bayesian networks [[electronic resource] /] / Carsten Riggelsen |
| Autore | Riggelsen Carsten |
| Pubbl/distr/stampa | Amsterdam ; ; Washington, DC, : IOS Press, c2008 |
| Descrizione fisica | 1 online resource (148 p.) |
| Disciplina |
519.5
519.5/42 |
| Collana |
Frontiers in artificial intelligence and applications
Dissertations in artificial intelligence |
| Soggetto topico |
Bayesian statistical decision theory
Machine learning Neural networks (Computer science) |
| Soggetto genere / forma | Electronic books. |
| ISBN |
6611733337
1-281-73333-4 9786611733339 1-60750-298-4 600-00-0346-3 1-4337-1131-1 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | Title page; Contents; Foreword; Introduction; Preliminaries; Learning Bayesian Networks from Data; Monte Carlo Methods and MCMC Simulation; Learning from Incomplete Data; Conclusion; References |
| Record Nr. | UNINA-9910453283703321 |
|
Riggelsen Carsten
|
||
| Amsterdam ; ; Washington, DC, : IOS Press, c2008 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Approximation methods for efficient learning of Bayesian networks [[electronic resource] /] / Carsten Riggelsen
| Approximation methods for efficient learning of Bayesian networks [[electronic resource] /] / Carsten Riggelsen |
| Autore | Riggelsen Carsten |
| Pubbl/distr/stampa | Amsterdam ; ; Washington, DC, : IOS Press, c2008 |
| Descrizione fisica | 1 online resource (148 p.) |
| Disciplina |
519.5
519.5/42 |
| Collana |
Frontiers in artificial intelligence and applications
Dissertations in artificial intelligence |
| Soggetto topico |
Bayesian statistical decision theory
Machine learning Neural networks (Computer science) |
| ISBN |
6611733337
1-281-73333-4 9786611733339 1-60750-298-4 600-00-0346-3 1-4337-1131-1 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | Title page; Contents; Foreword; Introduction; Preliminaries; Learning Bayesian Networks from Data; Monte Carlo Methods and MCMC Simulation; Learning from Incomplete Data; Conclusion; References |
| Record Nr. | UNINA-9910782592403321 |
|
Riggelsen Carsten
|
||
| Amsterdam ; ; Washington, DC, : IOS Press, c2008 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Approximation methods for efficient learning of Bayesian networks / / Carsten Riggelsen
| Approximation methods for efficient learning of Bayesian networks / / Carsten Riggelsen |
| Autore | Riggelsen Carsten |
| Edizione | [1st ed.] |
| Pubbl/distr/stampa | Amsterdam ; ; Washington, DC, : IOS Press, c2008 |
| Descrizione fisica | 1 online resource (148 p.) |
| Disciplina |
519.5
519.5/42 |
| Collana |
Frontiers in artificial intelligence and applications
Dissertations in artificial intelligence |
| Soggetto topico |
Bayesian statistical decision theory
Machine learning Neural networks (Computer science) |
| ISBN |
6611733337
1-281-73333-4 9786611733339 1-60750-298-4 600-00-0346-3 1-4337-1131-1 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | Title page; Contents; Foreword; Introduction; Preliminaries; Learning Bayesian Networks from Data; Monte Carlo Methods and MCMC Simulation; Learning from Incomplete Data; Conclusion; References |
| Record Nr. | UNINA-9910822048103321 |
|
Riggelsen Carsten
|
||
| Amsterdam ; ; Washington, DC, : IOS Press, c2008 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Bayes linear statistics : theory and methods / / Michael Goldstein and David Wooff
| Bayes linear statistics : theory and methods / / Michael Goldstein and David Wooff |
| Autore | Goldstein Michael <1949-> |
| Pubbl/distr/stampa | Chichester, England ; ; Hoboken, NJ, : John Wiley, c2007 |
| Descrizione fisica | 1 online resource (538 p.) |
| Disciplina | 519.5/42 |
| Altri autori (Persone) | WooffDavid |
| Collana | Wiley series in probability and statistics |
| Soggetto topico |
Bayesian statistical decision theory
Linear systems Computational complexity |
| ISBN |
1-280-85495-2
9786610854950 0-470-06566-4 0-470-06567-2 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Bayes Linear Statistics; Contents; Preface; 1 The Bayes linear approach; 1.1 Combining beliefs with data; 1.2 The Bayesian approach; 1.3 Features of the Bayes linear approach; 1.4 Example; 1.4.1 Expectation, variance, and standardization; 1.4.2 Prior inputs; 1.4.3 Adjusted expectations; 1.4.4 Adjusted versions; 1.4.5 Adjusted variances; 1.4.6 Checking data inputs; 1.4.7 Observed adjusted expectations; 1.4.8 Diagnostics for adjusted beliefs; 1.4.9 Further diagnostics for the adjusted versions; 1.4.10 Summary of basic adjustment; 1.4.11 Diagnostics for collections
1.4.12 Exploring collections of beliefs via canonical structure1.4.13 Modifying the original specifications; 1.4.14 Repeating the analysis for the revised model; 1.4.15 Global analysis of collections of observations; 1.4.16 Partial adjustments; 1.4.17 Partial diagnostics; 1.4.18 Summary; 1.5 Overview; 2 Expectation; 2.1 Expectation as a primitive; 2.2 Discussion: expectation as a primitive; 2.3 Quantifying collections of uncertainties; 2.4 Specifying prior beliefs; 2.4.1 Example: oral glucose tolerance test; 2.5 Qualitative and quantitative prior specification 2.6 Example: qualitative representation of uncertainty2.6.1 Identifying the quantities of interest; 2.6.2 Identifying relevant prior information; 2.6.3 Sources of variation; 2.6.4 Representing population variation; 2.6.5 The qualitative representation; 2.6.6 Graphical models; 2.7 Example: quantifying uncertainty; 2.7.1 Prior expectations; 2.7.2 Prior variances; 2.7.3 Prior covariances; 2.7.4 Summary of belief specifications; 2.8 Discussion: on the various methods for assigning expectations; 3 Adjusting beliefs; 3.1 Adjusted expectation; 3.2 Properties of adjusted expectation 3.3 Adjusted variance3.4 Interpretations of belief adjustment; 3.5 Foundational issues concerning belief adjustment; 3.6 Example: one-dimensional problem; 3.7 Collections of adjusted beliefs; 3.8 Examples; 3.8.1 Algebraic example; 3.8.2 Oral glucose tolerance test; 3.8.3 Many oral glucose tolerance tests; 3.9 Canonical analysis for a belief adjustment; 3.9.1 Canonical directions for the adjustment; 3.9.2 The resolution transform; 3.9.3 Partitioning the resolution; 3.9.4 The reverse adjustment; 3.9.5 Minimal linear sufficiency; 3.9.6 The adjusted belief transform matrix 3.10 The geometric interpretation of belief adjustment3.11 Examples; 3.11.1 Simple one-dimensional problem; 3.11.2 Algebraic example; 3.11.3 Oral glucose tolerance test; 3.12 Further reading; 4 The observed adjustment; 4.1 Discrepancy; 4.1.1 Discrepancy for a collection; 4.1.2 Evaluating discrepancy over a basis; 4.1.3 Discrepancy for quantities with variance zero; 4.2 Properties of discrepancy measures; 4.2.1 Evaluating the discrepancy vector over a basis; 4.3 Examples; 4.3.1 Simple one-dimensional problem; 4.3.2 Detecting degeneracy; 4.3.3 Oral glucose tolerance test 4.4 The observed adjustment |
| Record Nr. | UNINA-9910876938403321 |
|
Goldstein Michael <1949->
|
||
| Chichester, England ; ; Hoboken, NJ, : John Wiley, c2007 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Bayesian analysis of stochastic process models / / David Rios Insua, Fabrizio Ruggeri, Michael P. Wiper
| Bayesian analysis of stochastic process models / / David Rios Insua, Fabrizio Ruggeri, Michael P. Wiper |
| Autore | Rios Insua David <1964-> |
| Edizione | [1st edition] |
| Pubbl/distr/stampa | Hoboken, New Jersey, : Wiley, 2012 |
| Descrizione fisica | 1 online resource (316 p.) |
| Disciplina | 519.5/42 |
| Altri autori (Persone) |
WiperMichael P
RuggeriFabrizio |
| Collana | Wiley series in probability and statistics |
| Soggetto topico |
Bayesian statistical decision theory
Stochastic processes |
| ISBN |
1-280-58993-0
9786613619761 1-118-30403-9 0-470-97591-1 0-470-97592-X |
| Classificazione | MAT029010 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | Machine generated contents note: Preface 1 Stochastic Processes 11 1.1 Introduction 11 1.2 Key Concepts in Stochastic Processes 11 1.3 Main Classes of Stochastic Processes 16 1.4 Inference, Prediction and Decision Making 21 1.5 Discussion 23 2 Bayesian Analysis 27 2.1 Introduction 27 2.2 Bayesian Statistics 28 2.3 Bayesian Decision Analysis 37 2.4 Bayesian Computation 39 2.5 Discussion 51 3 Discrete Time Markov Chains 61 3.1 Introduction 61 3.2 Important Markov Chain Models 62 3.3 Inference for First Order Chains 66 3.4 Special Topics 76 3.5 Case Study: Wind Directions at Gij́on 87 3.6 Markov Decision Processes 94 3.7 Discussion 97 4 Continuous Time Markov Chains and Extensions 105 4.1 Introduction 105 4.2 Basic Setup and Results 106 4.3 Inference and Prediction for CTMCs 108 4.4 Case Study: Hardware Availability through CTMCs 112 4.5 Semi-Markovian Processes 118 4.6 Decision Making with Semi-Markovian Decision Processes 122 4.7 Discussion 128 5 Poisson Processes and Extensions 133 5.1 Introduction 133 5.2 Basics on Poisson Processes 134 5.3 Homogeneous Poisson Processes 138 5.4 Nonhomogeneous Poisson Processes 147 5.5 Compound Poisson Processes 153 5.6 Further Extensions of Poisson Processes 154 5.7 Case Study: Earthquake Occurrences 157 5.8 Discussion 162 6 Continuous Time Continuous Space Processes 169 6.1 Introduction 169 6.2 Gaussian Processes 170 6.3 Brownian Motion and Fractional Brownian Motion 174 6.4 Dilusions 181 6.5 Case Study: Prey-predator Systems 184 6.6 Discussion 190 7 Queueing Analysis 201 7.1 Introduction 201 7.2 Basic Queueing Concepts 201 7.3 The Main Queueing Models 204 7.4 Inference for Queueing Systems 208 7.5 Inference for M=M=1 Systems 209 7.6 Inference for Non Markovian Systems 220 7.7 Decision Problems in Queueing Systems 229 7.8 Case Study: Optimal Number of Beds in a Hospital 230 7.9 Discussion 235 8 Reliability 245 8.1 Introduction 245 8.2 Basic Reliability Concepts 246 8.3 Renewal Processes 249 8.4 Poisson Processes 251 8.5 Other Processes 259 8.6 Maintenance 262 8.7 Case Study: Gas Escapes 263 8.8 Discussion 271 9 Discrete Event Simulation 279 9.1 Introduction 279 9.2 Discrete Event Simulation Methods 280 9.3 A Bayesian View of DES 283 9.4 Case Study: A G=G=1 Queueing System 286 9.5 Bayesian Output Analysis 288 9.6 Simulation and Optimization 292 9.7 Discussion 294 10 Risk Analysis 301 10.1 Introduction 301 10.2 Risk Measures 302 10.3 Ruin Problems 316 10.4 Case Study: Ruin Probability Estimation 320 10.5 Discussion 327 Appendix A Main Distributions 337 Appendix B Generating Functions and the Laplace-Stieltjes Transform 347 Index. |
| Record Nr. | UNINA-9910141318903321 |
|
Rios Insua David <1964->
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| Hoboken, New Jersey, : Wiley, 2012 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Bayesian argumentation : the practical side of probability / / Frank Zenker, editor
| Bayesian argumentation : the practical side of probability / / Frank Zenker, editor |
| Edizione | [1st ed. 2013.] |
| Pubbl/distr/stampa | Dordrecht [Netherlands] ; ; New York, : Springer, 2013 |
| Descrizione fisica | 1 online resource (213 p.) |
| Disciplina |
519.5/42
519.542 |
| Altri autori (Persone) | ZenkerFrank |
| Collana | Synthese library : studies in epistemology, logic, methodology, and philosophy of science |
| Soggetto topico |
Bayesian statistical decision theory
Evidence (Law) Evidence, Criminal |
| ISBN |
1-283-93609-7
94-007-5357-8 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | pt. 1. The Bayesian approach to argumentation -- pt. 2. The legal domain -- pt 3. Modeling rational agents -- pt. 4. Theoretical issues. |
| Record Nr. | UNINA-9910438240903321 |
| Dordrecht [Netherlands] ; ; New York, : Springer, 2013 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
