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Bayesian analysis for the social sciences [[electronic resource] /] / Simon Jackman
| Bayesian analysis for the social sciences [[electronic resource] /] / Simon Jackman |
| Autore | Jackman Simon <1966-> |
| Pubbl/distr/stampa | Hoboken, NJ, : Wiley, 2009 |
| Descrizione fisica | 1 online resource (609 p.) |
| Disciplina |
519.5
519.542 |
| Collana | Wiley series in probability and statistics. |
| Soggetto topico |
Social sciences - Statistical methods
Bayesian statistical decision theory |
| Soggetto genere / forma | Electronic books. |
| ISBN |
1-282-35479-5
9786612354793 0-470-68662-6 0-470-68663-4 |
| Formato | Materiale a stampa  |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Bayesian Analysis for the Social Sciences; Contents; List of Figures; List of Tables; Preface; Acknowledgments; Introduction; Part I Introducing Bayesian Analysis; 1 The foundations of Bayesian inference; 1.1 What is probability?; 1.1.1 Probability in classical statistics; 1.1.2 Subjective probability; 1.2 Subjective probability in Bayesian statistics; 1.3 Bayes theorem, discrete case; 1.4 Bayes theorem, continuous parameter; 1.4.1 Conjugate priors; 1.4.2 Bayesian updating with irregular priors; 1.4.3 Cromwell's Rule; 1.4.4 Bayesian updating as information accumulation
1.5 Parameters as random variables, beliefs as distributions1.6 Communicating the results of a Bayesian analysis; 1.6.1 Bayesian point estimation; 1.6.2 Credible regions; 1.7 Asymptotic properties of posterior distributions; 1.8 Bayesian hypothesis testing; 1.8.1 Model choice; 1.8.2 Bayes factors; 1.9 From subjective beliefs to parameters and models; 1.9.1 Exchangeability; 1.9.2 Implications and extensions of de Finetti's Representation Theorem; 1.9.3 Finite exchangeability; 1.9.4 Exchangeability and prediction; 1.9.5 Conditional exchangeability and multiparameter models 1.9.6 Exchangeability of parameters: hierarchical modeling1.10 Historical note; 2 Getting started: Bayesian analysis for simple models; 2.1 Learning about probabilities, rates and proportions; 2.1.1 Conjugate priors for probabilities, rates and proportions; 2.1.2 Bayes estimates as weighted averages of priors and data; 2.1.3 Parameterizations and priors; 2.1.4 The variance of the posterior density; 2.2 Associations between binary variables; 2.3 Learning from counts; 2.3.1 Predictive inference with count data; 2.4 Learning about a normal mean and variance; 2.4.1 Variance known 2.4.2 Mean and variance unknown2.4.3 Conditionally conjugate prior; 2.4.4 An improper, reference prior; 2.4.5 Conflict between likelihood and prior; 2.4.6 Non-conjugate priors; 2.5 Regression models; 2.5.1 Bayesian regression analysis; 2.5.2 Likelihood function; 2.5.3 Conjugate prior; 2.5.4 Improper, reference prior; 2.6 Further reading; Part II Simulation Based Bayesian Analysis; 3 Monte Carlo methods; 3.1 Simulation consistency; 3.2 Inference for functions of parameters; 3.3 Marginalization via Monte Carlo integration; 3.4 Sampling algorithms; 3.4.1 Inverse-CDF method 3.4.2 Importance sampling3.4.3 Accept-reject sampling; 3.4.4 Adaptive rejection sampling; 3.5 Further reading; 4 Markov chains; 4.1 Notation and definitions; 4.1.1 State space; 4.1.2 Transition kernel; 4.2 Properties of Markov chains; 4.2.1 Existence of a stationary distribution, discrete case; 4.2.2 Existence of a stationary distribution, continuous case; 4.2.3 Irreducibility; 4.2.4 Recurrence; 4.2.5 Invariant measure; 4.2.6 Reversibility; 4.2.7 Aperiodicity; 4.3 Convergence of Markov chains; 4.3.1 Speed of convergence; 4.4 Limit theorems for Markov chains; 4.4.1 Simulation inefficiency 4.4.2 Central limit theorems for Markov chains |
| Record Nr. | UNINA-9910139959903321 |
Jackman Simon <1966->
|
||
| Hoboken, NJ, : Wiley, 2009 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Bayesian analysis for the social sciences [[electronic resource] /] / Simon Jackman
| Bayesian analysis for the social sciences [[electronic resource] /] / Simon Jackman |
| Autore | Jackman Simon <1966-> |
| Pubbl/distr/stampa | Hoboken, NJ, : Wiley, 2009 |
| Descrizione fisica | 1 online resource (609 p.) |
| Disciplina |
519.5
519.542 |
| Collana | Wiley series in probability and statistics. |
| Soggetto topico |
Social sciences - Statistical methods
Bayesian statistical decision theory |
| ISBN |
1-282-35479-5
9786612354793 0-470-68662-6 0-470-68663-4 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Bayesian Analysis for the Social Sciences; Contents; List of Figures; List of Tables; Preface; Acknowledgments; Introduction; Part I Introducing Bayesian Analysis; 1 The foundations of Bayesian inference; 1.1 What is probability?; 1.1.1 Probability in classical statistics; 1.1.2 Subjective probability; 1.2 Subjective probability in Bayesian statistics; 1.3 Bayes theorem, discrete case; 1.4 Bayes theorem, continuous parameter; 1.4.1 Conjugate priors; 1.4.2 Bayesian updating with irregular priors; 1.4.3 Cromwell's Rule; 1.4.4 Bayesian updating as information accumulation
1.5 Parameters as random variables, beliefs as distributions1.6 Communicating the results of a Bayesian analysis; 1.6.1 Bayesian point estimation; 1.6.2 Credible regions; 1.7 Asymptotic properties of posterior distributions; 1.8 Bayesian hypothesis testing; 1.8.1 Model choice; 1.8.2 Bayes factors; 1.9 From subjective beliefs to parameters and models; 1.9.1 Exchangeability; 1.9.2 Implications and extensions of de Finetti's Representation Theorem; 1.9.3 Finite exchangeability; 1.9.4 Exchangeability and prediction; 1.9.5 Conditional exchangeability and multiparameter models 1.9.6 Exchangeability of parameters: hierarchical modeling1.10 Historical note; 2 Getting started: Bayesian analysis for simple models; 2.1 Learning about probabilities, rates and proportions; 2.1.1 Conjugate priors for probabilities, rates and proportions; 2.1.2 Bayes estimates as weighted averages of priors and data; 2.1.3 Parameterizations and priors; 2.1.4 The variance of the posterior density; 2.2 Associations between binary variables; 2.3 Learning from counts; 2.3.1 Predictive inference with count data; 2.4 Learning about a normal mean and variance; 2.4.1 Variance known 2.4.2 Mean and variance unknown2.4.3 Conditionally conjugate prior; 2.4.4 An improper, reference prior; 2.4.5 Conflict between likelihood and prior; 2.4.6 Non-conjugate priors; 2.5 Regression models; 2.5.1 Bayesian regression analysis; 2.5.2 Likelihood function; 2.5.3 Conjugate prior; 2.5.4 Improper, reference prior; 2.6 Further reading; Part II Simulation Based Bayesian Analysis; 3 Monte Carlo methods; 3.1 Simulation consistency; 3.2 Inference for functions of parameters; 3.3 Marginalization via Monte Carlo integration; 3.4 Sampling algorithms; 3.4.1 Inverse-CDF method 3.4.2 Importance sampling3.4.3 Accept-reject sampling; 3.4.4 Adaptive rejection sampling; 3.5 Further reading; 4 Markov chains; 4.1 Notation and definitions; 4.1.1 State space; 4.1.2 Transition kernel; 4.2 Properties of Markov chains; 4.2.1 Existence of a stationary distribution, discrete case; 4.2.2 Existence of a stationary distribution, continuous case; 4.2.3 Irreducibility; 4.2.4 Recurrence; 4.2.5 Invariant measure; 4.2.6 Reversibility; 4.2.7 Aperiodicity; 4.3 Convergence of Markov chains; 4.3.1 Speed of convergence; 4.4 Limit theorems for Markov chains; 4.4.1 Simulation inefficiency 4.4.2 Central limit theorems for Markov chains |
| Record Nr. | UNINA-9910831183403321 |
|
Jackman Simon <1966->
|
||
| Hoboken, NJ, : Wiley, 2009 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Bayesian analysis for the social sciences / / Simon Jackman
| Bayesian analysis for the social sciences / / Simon Jackman |
| Autore | Jackman Simon <1966-> |
| Pubbl/distr/stampa | Hoboken, NJ, : Wiley, 2009 |
| Descrizione fisica | 1 online resource (609 p.) |
| Disciplina |
519.5
519.542 |
| Collana | Wiley series in probability and statistics. |
| Soggetto topico |
Social sciences - Statistical methods
Bayesian statistical decision theory |
| ISBN |
1-282-35479-5
9786612354793 0-470-68662-6 0-470-68663-4 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Bayesian Analysis for the Social Sciences; Contents; List of Figures; List of Tables; Preface; Acknowledgments; Introduction; Part I Introducing Bayesian Analysis; 1 The foundations of Bayesian inference; 1.1 What is probability?; 1.1.1 Probability in classical statistics; 1.1.2 Subjective probability; 1.2 Subjective probability in Bayesian statistics; 1.3 Bayes theorem, discrete case; 1.4 Bayes theorem, continuous parameter; 1.4.1 Conjugate priors; 1.4.2 Bayesian updating with irregular priors; 1.4.3 Cromwell's Rule; 1.4.4 Bayesian updating as information accumulation
1.5 Parameters as random variables, beliefs as distributions1.6 Communicating the results of a Bayesian analysis; 1.6.1 Bayesian point estimation; 1.6.2 Credible regions; 1.7 Asymptotic properties of posterior distributions; 1.8 Bayesian hypothesis testing; 1.8.1 Model choice; 1.8.2 Bayes factors; 1.9 From subjective beliefs to parameters and models; 1.9.1 Exchangeability; 1.9.2 Implications and extensions of de Finetti's Representation Theorem; 1.9.3 Finite exchangeability; 1.9.4 Exchangeability and prediction; 1.9.5 Conditional exchangeability and multiparameter models 1.9.6 Exchangeability of parameters: hierarchical modeling1.10 Historical note; 2 Getting started: Bayesian analysis for simple models; 2.1 Learning about probabilities, rates and proportions; 2.1.1 Conjugate priors for probabilities, rates and proportions; 2.1.2 Bayes estimates as weighted averages of priors and data; 2.1.3 Parameterizations and priors; 2.1.4 The variance of the posterior density; 2.2 Associations between binary variables; 2.3 Learning from counts; 2.3.1 Predictive inference with count data; 2.4 Learning about a normal mean and variance; 2.4.1 Variance known 2.4.2 Mean and variance unknown2.4.3 Conditionally conjugate prior; 2.4.4 An improper, reference prior; 2.4.5 Conflict between likelihood and prior; 2.4.6 Non-conjugate priors; 2.5 Regression models; 2.5.1 Bayesian regression analysis; 2.5.2 Likelihood function; 2.5.3 Conjugate prior; 2.5.4 Improper, reference prior; 2.6 Further reading; Part II Simulation Based Bayesian Analysis; 3 Monte Carlo methods; 3.1 Simulation consistency; 3.2 Inference for functions of parameters; 3.3 Marginalization via Monte Carlo integration; 3.4 Sampling algorithms; 3.4.1 Inverse-CDF method 3.4.2 Importance sampling3.4.3 Accept-reject sampling; 3.4.4 Adaptive rejection sampling; 3.5 Further reading; 4 Markov chains; 4.1 Notation and definitions; 4.1.1 State space; 4.1.2 Transition kernel; 4.2 Properties of Markov chains; 4.2.1 Existence of a stationary distribution, discrete case; 4.2.2 Existence of a stationary distribution, continuous case; 4.2.3 Irreducibility; 4.2.4 Recurrence; 4.2.5 Invariant measure; 4.2.6 Reversibility; 4.2.7 Aperiodicity; 4.3 Convergence of Markov chains; 4.3.1 Speed of convergence; 4.4 Limit theorems for Markov chains; 4.4.1 Simulation inefficiency 4.4.2 Central limit theorems for Markov chains |
| Record Nr. | UNINA-9910877722203321 |
|
Jackman Simon <1966->
|
||
| Hoboken, NJ, : Wiley, 2009 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||