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Fundamentals of Nonparametric Bayesian Inference
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Artikel-Nr:
9780521878265
Veröffentl:
2019
Einband:
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Erscheinungsdatum:
19.03.2019
Seiten:
672
Autor:
Subhashis Ghosal
Gewicht:
1440 g
Format:
260x183x40 mm
Sprache:
Englisch
Beschreibung:
Subhashis Ghosal is Professor of Statistics at North Carolina State University. His primary research interest is in the theory, methodology and various applications of Bayesian nonparametrics. He has edited one book, written nearly one hundred papers, and serves on the editorial boards of the Annals of Statistics, Bernoulli, and the Electronic Journal of Statistics. He is an elected fellow of the Institute of Mathematical Statistics, the American Statistical Association and the International Society for Bayesian Analysis.
Bayesian nonparametrics comes of age with this landmark text synthesizing theory, methodology and computation.
Preface; Glossary of symbols; 1. Introduction; 2. Priors on function spaces; 3. Priors on spaces of probability measures; 4. Dirichlet processes; 5. Dirichlet process mixtures; 6. Consistency: general theory; 7. Consistency: examples; 8. Contraction rates: general theory; 9. Contraction rates: examples; 10. Adaptation and model selection; 11. Gaussian process priors; 12. Infinite-dimensional Bernstein-von Mises theorem; 13. Survival analysis; 14. Discrete random structures; Appendices; References; Author index; Subject index.