Pubbl/distr/stampa	Wrocław, : Wydawn. Politechniki Wrocławskiej
Descrizione fisica	1 online resource
Soggetto topico	Statistical decision Decision making Zarządzanie Badania operacyjne
Soggetto genere / forma	Periodicals.
ISSN	2391-6060
Formato	Materiale a stampa
Livello bibliografico	Periodico
Lingua di pubblicazione	pol
Record Nr.	UNISA-996525701903316

Autore	Bernardo J. M
Pubbl/distr/stampa	Chichester ; ; New York, : Wiley, c2000
Descrizione fisica	1 online resource (611 p.)
Disciplina	519.5 519.542
Altri autori (Persone)	SmithAdrian F. M
Collana	Wiley series in probability and statistics
Soggetto topico	Bayesian statistical decision theory Statistical decision
ISBN	1-282-30786-X 9786612307867 0-470-31687-X 0-470-31771-X
Formato	Materiale a stampa
Livello bibliografico	Monografia
Lingua di pubblicazione	eng
Nota di contenuto	BAYESIAN THEORY; Contents; 1. INTRODUCTION; 1.1. Thomas Bayes; 1.2. The subjectivist view of probability; 1.3. Bayesian Statistics in perspective; 1.4. An overview of Bayesian Theory; 1.4.1. Scope; 1.4.2. Foundations; 1.4.3. Generalisations; 1.4.4. Modelling; 1.4.5. Inference; 1.4.6. Remodelling; 1.4.7. Basic formulae; 1.4.8. Non-Bayesian theories; 1.5. A Bayesian reading list; 2. FOUNDATIONS; 2.1. Beliefs and actions; 2.2. Decision problems; 2.2.1. Basic elements; 2.2.2. Formal representation; 2.3. Coherence and quantification; 2.3.1. Events, options and preferences 2.3.2. Coherent preferences2.3.3. Quantification; 2.4. Beliefs and probabilities; 2.4.1. Representation of beliefs; 2.4.2. Revision of beliefs and Bayes' theorem; 2.4.3. Conditional independence; 2.4.4. Sequential revision of beliefs; 2.5. Actions and utilities; 2.5.1. Bounded sets of consequences; 2.5.2. Bounded decision problems; 2.5.3. General decision problems; 2.6. Sequential decision problems; 2.6.1. Complex decision problems; 2.6.2. Backward induction; 2.6.3. Design of experiments; 2.7. Inference and information; 2.7.1. Reporting beliefs as a decision problem 2.7.2. The utility of a probability distribution2.7.3. Approximation and discrepancy; 2.7.4. Information; 2.8. Discussion and further references; 2.8.1. Operational definitions; 2.8.2. Quantitative coherence theories; 2.8.3. Related theories; 2.8.4. Critical issues; 3. GENERALISATIONS; 3.1. Generalised representation of beliefs; 3.1.1. Motivation; 3.1.2. Countable additivity; 3.2. Review of probability theory; 3.2.1. Random quantities and distributions; 3.2.2. Some particular univariate distributions; 3.2.3. Convergence and limit theorems; 3.2.4. Random vectors, Bayes' theorem 3.2.5. Some particular multivariate distributions3.3. Generalised options and utilities; 3.3.1. Motivation and preliminaries; 3.3.2. Generalised preferences; 3.3.3. The value of information; 3.4. Generalised information measures; 3.4.1. The general problem of reporting beliefs; 3.4.2. The utility of a general probability distribution; 3.4.3. Generalised approximation and discrepancy; 3.4.4. Generalised information; 3.5. Discussion and further references; 3.5.1. The role of mathematics; 3.5.2. Critical issues; 4. MODELLING; 4.1 Statistical models; 4.1.1. Beliefs and models 4.2. Exchangeability and related concepts4.2.1. Dependence and independence; 4.2.2. Exchangeability and partial exchangeability; 4.3. Models via exchangeability; 4.3.1. The Bernoulli and binomial models; 4.3.2. The multinomial model; 4.3.3. The general model; 4.4. Models via invariance; 4.4.1. The normal model; 4.4.2. The multivariate normal model; 4.4.3. The exponential model; 4.4.4. The geometric model; 4.5. Models via sufficient statistics; 4.5.1. Summary statistics; 4.5.2. Predictive sufficiency and parametric sufficiency; 4.5.3. Sufficiency and the exponential family 4.5.4. Information measures and the exponential family
Record Nr.	UNINA-9910139987903321

Autore	Groebner
Edizione	[Ninth edition.]
Pubbl/distr/stampa	Harlow, England : , : Pearson, , 2014
Descrizione fisica	1 online resource (106 pages) : illustrations (some color)
Disciplina	519.5
Collana	Pearson New International Edition
Soggetto topico	Commercial statistics Statistical decision
ISBN	1-292-03652-4
Formato	Materiale a stampa
Livello bibliografico	Monografia
Lingua di pubblicazione	eng
Nota di contenuto	Cover -- Table of Contents -- 1. The Where, Why, and How of Data Collection -- 2. Graphs, Charts, and Tables - Describing Your Data -- 3. Describing Data Using Numerical Measures -- 4. Special Review Section I -- 5. Introduction to Probability -- 6. Discrete Probability Distributions -- 7. Introduction to Continuous Probability Distributions -- 8. Introduction to Sampling Distributions -- 9. Estimating Single Population Parameters -- 10. Introduction to Hypothesis Testing -- 11. Estimation and Hypothesis Testing for Two Population Parameters -- 12. Hypothesis Tests and Estimation for Population Variances -- 13. Analysis of Variance -- 14. Special Review Section II -- 15. Goodness-of-Fit Tests and Contingency Analysis -- 16. Introduction to Linear Regression and Correlation Analysis -- 17. Multiple Regression Analysis and Model Building -- 18. Analyzing and Forecasting Time-Series Data -- 19. Introduction to Nonparametric Statistics -- 20. Introduction to Quality and Statistical Process Control -- Index -- 4.
Record Nr.	UNINA-9910153096803321

Edizione	[1st edition]
Pubbl/distr/stampa	Chichester, West Sussex, : John Wiley & Sons Inc., 2012
Descrizione fisica	1 online resource (499 p.)
Disciplina	519.5/42
Altri autori (Persone)	AlstonClair MengersenKerrie L PettittAnthony (Anthony N.)
Collana	Wiley Series in Probability and Statistics
Soggetto topico	Bayesian statistical decision theory Statistical decision
ISBN	1-118-39447-X 1-283-65634-5 1-118-39449-6
Formato	Materiale a stampa
Livello bibliografico	Monografia
Lingua di pubblicazione	eng
Nota di contenuto	Case 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 2.3.4 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 3.3 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 4 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 5.1 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 Acknowledgements
Record Nr.	UNINA-9910141404103321

Autore	Resnik Michael D
Pubbl/distr/stampa	Minneapolis, : University of Minnesota Press, c1987
Descrizione fisica	1 online resource (238 p.)
Disciplina	001.53/8
Soggetto topico	Decision making Statistical decision
Soggetto genere / forma	Electronic books.
ISBN	0-8166-8231-3
Formato	Materiale a stampa
Livello bibliografico	Monografia
Lingua di pubblicazione	eng
Nota di contenuto	CONTENTS; PREFACE; ACKNOWLEDGMENTS; Chapter 1 Introduction; Chapter 2 Decisions under Ignorance; Chapter 3 Decisions under Risk: Probability; Chapter 4 Decisions under Risk: Utility; Chapter 5 Game Theory; Chapter 6 Social Choices; BIBLIOGRAPHY; INDEX
Record Nr.	UNINA-9910457914603321