LEADER 06013nam 22006615 450 001 9910299964703321 005 20200703034041.0 010 $a3-319-02084-6 024 7 $a10.1007/978-3-319-02084-6 035 $a(CKB)3710000000074712 035 $a(EBL)1592556 035 $a(OCoLC)871776275 035 $a(SSID)ssj0001067315 035 $a(PQKBManifestationID)11705945 035 $a(PQKBTitleCode)TC0001067315 035 $a(PQKBWorkID)11081101 035 $a(PQKB)11772924 035 $a(MiAaPQ)EBC1592556 035 $a(DE-He213)978-3-319-02084-6 035 $a(PPN)176105417 035 $a(EXLCZ)993710000000074712 100 $a20131122d2014 u| 0 101 0 $aeng 135 $aur|n|---||||| 181 $ctxt 182 $cc 183 $acr 200 14$aThe Contribution of Young Researchers to Bayesian Statistics $eProceedings of BAYSM2013 /$fedited by Ettore Lanzarone, Francesca Ieva 205 $a1st ed. 2014. 210 1$aCham :$cSpringer International Publishing :$cImprint: Springer,$d2014. 215 $a1 online resource (195 p.) 225 1 $aSpringer Proceedings in Mathematics & Statistics,$x2194-1009 ;$v63 300 $aDescription based upon print version of record. 311 $a3-319-02083-8 320 $aIncludes bibliographical references and index. 327 $aPreface; 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 327 $a4.1 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 327 $a6.3.1 Dataset Simulation6.3.2 Validation of the RBPF Algorithm; 6.4 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 327 $a10.1 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 327 $a13 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 327 $a16 Parallel Slice Sampling 330 $aThe first Bayesian Young Statisticians Meeting, BAYSM 2013, has provided a unique opportunity for young researchers, M.S. students, Ph.D. students, and post-docs dealing with Bayesian statistics to connect with the Bayesian community at large, exchange ideas, and network with scholars working in their field. The Workshop, which took place June 5th and 6th 2013 at CNR-IMATI, Milan, has promoted further research in all the fields where Bayesian statistics may be employed under the guidance of renowned plenary lecturers and senior discussants. A selection of the contributions to the meeting and the summary of one of the plenary lectures compose this volume. . 410 0$aSpringer Proceedings in Mathematics & Statistics,$x2194-1009 ;$v63 606 $aStatistics  606 $aStatistical Theory and Methods$3https://scigraph.springernature.com/ontologies/product-market-codes/S11001 606 $aStatistics, general$3https://scigraph.springernature.com/ontologies/product-market-codes/S0000X 606 $aStatistics for Social Sciences, Humanities, Law$3https://scigraph.springernature.com/ontologies/product-market-codes/S17040 615 0$aStatistics . 615 14$aStatistical Theory and Methods. 615 24$aStatistics, general. 615 24$aStatistics for Social Sciences, Humanities, Law. 676 $a519.5 702 $aLanzarone$b Ettore$4edt$4http://id.loc.gov/vocabulary/relators/edt 702 $aIeva$b Francesca$4edt$4http://id.loc.gov/vocabulary/relators/edt 906 $aBOOK 912 $a9910299964703321 996 $aThe Contribution of Young Researchers to Bayesian Statistics$92536877 997 $aUNINA