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Record Nr. |
UNINA9910299964703321 |
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Titolo |
The Contribution of Young Researchers to Bayesian Statistics : Proceedings of BAYSM2013 / / edited by Ettore Lanzarone, Francesca Ieva |
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Pubbl/distr/stampa |
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Cham : , : Springer International Publishing : , : Imprint : Springer, , 2014 |
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ISBN |
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Edizione |
[1st ed. 2014.] |
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Descrizione fisica |
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1 online resource (195 p.) |
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Collana |
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Springer Proceedings in Mathematics & Statistics, , 2194-1009 ; ; 63 |
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Disciplina |
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Soggetti |
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Statistics |
Statistical Theory and Methods |
Statistics, general |
Statistics for Social Sciences, Humanities, Law |
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Lingua di pubblicazione |
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Formato |
Materiale a stampa |
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Livello bibliografico |
Monografia |
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Note generali |
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Description based upon print version of record. |
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Nota di bibliografia |
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Includes bibliographical references and index. |
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Nota di contenuto |
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Preface; Contents; Part I Theoretical Bayes ; 1 A Nonparametric Model for Stationary Time Series; 1.1 Introduction; 1.2 The Model; 1.2.1 Illustrations; References; 2 Estimation of Optimally Combined-Biomarker Accuracy in the Absence of a Gold Standard Reference Test; 2.1 Introduction; 2.2 Methods; 2.3 Results; 2.4 Conclusions; References; 3 On Bayesian Transformation Selection:Problem Formulation and Preliminary Results; 3.1 Introduction; 3.2 Bayesian Formulation; 3.3 Results; 3.4 Conclusions; References; 4 A Simple Proof for the Multinomial Version of the Representation Theorem |
4.1 Introduction4.2 De Finetti's Method for Multinomial Trials; References; 5 A Sequential Monte Carlo Framework for Adaptive Bayesian Model Discrimination Designs Using MutualInformation; 5.1 Introduction; 5.2 Notation; 5.3 Sequential Monte Carlo Incorporating Model Uncertainty; 5.4 Mutual Information for Model Discrimination; 5.5 Examples; 5.6 Conclusion; References; 6 Joint Parameter Estimation and Biomass Tracking in a Stochastic Predator-Prey System; 6.1 Introduction; 6.2 Method; 6.2.1 State-Space Model; 6.2.2 Rao-Blackwellized Particle Filter; 6.3 Experimental Results |
6.3.1 Dataset Simulation6.3.2 Validation of the RBPF Algorithm; 6.4 |
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Conclusions; References; 7 Adaptive Bayes Test for Monotonicity; 7.1 Introduction; 7.2 Theoretical Results; 7.3 Conclusion; References; 8 Bayesian Inference on Individual-Based Models by Controlling the Random Inputs; 8.1 Introduction; 8.2 Controlling Random Inputs; 8.3 Woodhoopoe Model; 8.4 Summary of the Talk; References; 9 Consistency of Bayesian Nonparametric Hidden Markov Models; 9.1 Introduction; 9.2 The Model; 9.3 Consistency; References; 10 Bayesian Methodology in the Stochastic Event Reconstruction Problems |
10.1 Introduction10.2 Theoretical Preliminaries; 10.3 Methods and Results; References; Part II Computational Bayes ; 11 Efficient Fitting of Bayesian Regression Models with Spatio-Temporally Varying Coefficients; 11.1 Introduction; 11.2 A Spatio-Temporal Model; 11.2.1 Parameterisation, Marginalisation and Interweaving; 11.2.2 Model Specifications; 11.3 Results; 11.4 Summary; References; 12 PAWL-Forced Simulated Tempering; 12.1 A Parallel Adaptive Wang-Landau Algorithm; 12.2 Simulated Tempering; 12.3 Conclusion; References |
13 Approximate Bayesian Computation for the Elimination of Nuisance Parameters13.1 Introduction; 13.2 The Elimination of Nuisance Parameters; 13.2.1 Examples; 13.3 Conclusions; References; 14 Reweighting Schemes Based on Particle Methods; 14.1 Introduction; 14.2 Particle Move-Reweighting Strategies; 14.3 Closing Remarks; References; 15 A Bayesian Nonparametric Framework to Inference on Totals of Finite Populations; 15.1 Introduction; 15.2 Inference on Planned Domains; 15.2.1 Posterior Point Estimates; 15.2.2 Full Posterior Inference; 15.3 Simulation Results; 15.4 Discussion; References |
16 Parallel Slice Sampling |
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Sommario/riassunto |
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The first Bayesian Young Statisticians Meeting, BAYSM 2013, has provided a unique opportunity for young researchers, M.S. students, Ph.D. students, and post-docs dealing with Bayesian statistics to connect with the Bayesian community at large, exchange ideas, and network with scholars working in their field. The Workshop, which took place June 5th and 6th 2013 at CNR-IMATI, Milan, has promoted further research in all the fields where Bayesian statistics may be employed under the guidance of renowned plenary lecturers and senior discussants. A selection of the contributions to the meeting and the summary of one of the plenary lectures compose this volume. . |
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