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Badania operacyjne i decyzje = : Operations research and decision
| Badania operacyjne i decyzje = : Operations research and decision |
| 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 |
| Wrocław, : Wydawn. Politechniki Wrocławskiej | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. di Salerno | ||
| ||
Badania operacyjne i decyzje = : Operations research and decision
| Badania operacyjne i decyzje = : Operations research and decision |
| 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. | UNINA-9910892964603321 |
| Wrocław, : Wydawn. Politechniki Wrocławskiej | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Bayesian reasoning in data analysis : a critical introduction / / Giulio D'Agostini
| Bayesian reasoning in data analysis : a critical introduction / / Giulio D'Agostini |
| Autore | D'Agostini G (Giulio) |
| Pubbl/distr/stampa | Singapore ; ; River Edge, NJ, : World Scientific, c2003 |
| Descrizione fisica | 1 online resource (351 p.) |
| Disciplina | 519.5/42 |
| Soggetto topico |
Bayesian statistical decision theory
Statistical decision |
| ISBN |
9786611928216
9781281928214 1281928216 9789812775511 981277551X |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Contents; Preface; PART I Critical review and outline of the Bayesian alternative; 1 Uncertainty in physics and the usual methods of handling it; 1.1 Uncertainty in physics; 1.2 True value, error and uncertainty; 1.3 Sources of measurement uncertainty; 1.4 Usual handling of measurement uncertainties; 1.5 Probability of observables versus probability of 'true values'; 1.6 Probability of the causes; 1.7 Unsuitability of frequentistic confidence intervals; 1.8 Misunderstandings caused by the standard paradigm of hypothesis tests; 1.9 Statistical significance versus probability of hypotheses
2 A probabilistic theory of measurement uncertainty2.1 Where to restart from?; 2.2 Concepts of probability; 2.3 Subjective probability; 2.4 Learning from observations: the 'problem of induction'; 2.5 Beyond Popper's falsification scheme; 2.6 From the probability of the effects to the probability of the causes; 2.7 Bayes' theorem for uncertain quantities: derivation from a physicist's point of view; 2.8 Afraid of 'prejudices'? Logical necessity versus frequent practical irrelevance of the priors; 2.9 Recovering standard methods and short-cuts to Bayesian reasoning 2.10 Evaluation of measurement uncertainty: general scheme2.10.1 Direct measurement in the absence of systematic errors; 2.10.2 Indirect measurements; 2.10.3 Systematic errors; 2.10.4 Approximate methods; PART 2 A Bayesian primer; 3 Subjective probability and Bayes' theorem; 3.1 What is probability?; 3.2 Subjective definition of probability; 3.3 Rules of probability; 3.4 Subjective probability and 'objective' description of the physical world; 3.5 Conditional probability and Bayes' theorem; 3.5.1 Dependence of the probability on the state of information; 3.5.2 Conditional probability 3.5.3 Bayes' theorem3.5.4 'Conventional' use of Bayes' theorem; 3.6 Bayesian statistics: learning by experience; 3.7 Hypothesis 'test' (discrete case); 3.7.1 Variations over a problem to Newton; 3.8 Falsificationism and Bayesian statistics; 3.9 Probability versus decision; 3.10 Probability of hypotheses versus probability of observations; 3.11 Choice of the initial probabilities (discrete case); 3.11.1 General criteria; 3.11.2 Insufficient reason and Maximum Entropy; 3.12 Solution to some problems; 3.12.1 AIDS test; 3.12.2 Gold/silver ring problem; 3.12.3 Regular or double-head coin? 3.12.4 Which random generator is responsible for the observed number?3.13 Some further examples showing the crucial role of background knowledge; 4 Probability distributions (a concise reminder); 4.1 Discrete variables; 4.2 Continuous variables: probability and probability density function; 4.3 Distribution of several random variables; 4.4 Propagation of uncertainty; 4.5 Central limit theorem; 4.5.1 Terms and role; 4.5.2 Distribution of a sample average; 4.5.3 Normal approximation of the binomial and of the Poisson distribution; 4.5.4 Normal distribution of measurement errors; 4.5.5 Caution 4.6 Laws of large numbers |
| Record Nr. | UNINA-9911049052503321 |
D'Agostini G (Giulio)
|
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| Singapore ; ; River Edge, NJ, : World Scientific, c2003 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Bayesian theory [[electronic resource] /] / José M. Bernardo, Adrian F.M. Smith
| Bayesian theory [[electronic resource] /] / José M. Bernardo, Adrian F.M. Smith |
| 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 |
|
Bernardo J. M
|
||
| Chichester ; ; New York, : Wiley, c2000 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Bayesian theory [[electronic resource] /] / José M. Bernardo, Adrian F.M. Smith
| Bayesian theory [[electronic resource] /] / José M. Bernardo, Adrian F.M. Smith |
| 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. | UNISA-996207750003316 |
|
Bernardo J. M
|
||
| Chichester ; ; New York, : Wiley, c2000 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. di Salerno | ||
| ||
Bayesian theory [[electronic resource] /] / José M. Bernardo, Adrian F.M. Smith
| Bayesian theory [[electronic resource] /] / José M. Bernardo, Adrian F.M. Smith |
| 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-9910830888603321 |
|
Bernardo J. M
|
||
| Chichester ; ; New York, : Wiley, c2000 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Bayesian theory / / Jose M. Bernardo, Adrian F.M. Smith
| Bayesian theory / / Jose M. Bernardo, Adrian F.M. Smith |
| 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 |
9786612307867
9781282307865 128230786X 9780470316870 047031687X 9780470317716 047031771X |
| 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-9911019809403321 |
|
Bernardo J. M
|
||
| Chichester ; ; New York, : Wiley, c2000 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Business statistics : a decision-making approach / / Groebner, Shannon, Fry
| Business statistics : a decision-making approach / / Groebner, Shannon, Fry |
| 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 |
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Groebner
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| Harlow, England : , : Pearson, , 2014 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Case studies in Bayesian statistical modelling and analysis [[electronic resource] /] / edited by Clair Alston, Kerrie Mengersen, and Anthony Pettitt
| Case studies in Bayesian statistical modelling and analysis [[electronic resource] /] / edited by Clair Alston, Kerrie Mengersen, and Anthony Pettitt |
| 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 |
| Chichester, West Sussex, : John Wiley & Sons Inc., 2012 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Case studies in Bayesian statistical modelling and analysis / / edited by Clair Alston, Kerrie Mengersen, and Anthony Pettitt
| Case studies in Bayesian statistical modelling and analysis / / edited by Clair Alston, Kerrie Mengersen, and Anthony Pettitt |
| 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 |
9781118394472
111839447X 9781283656344 1283656345 9781118394496 1118394496 |
| 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-9910819512103321 |
| Chichester, West Sussex, : John Wiley & Sons Inc., 2012 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
