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020 _a9783031613296
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024 7 _a10.1007/978-3-031-61329-6
_2doi
050 4 _aQA276-280
072 7 _aPBT
_2bicssc
072 7 _aMAT029000
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072 7 _aPBT
_2thema
245 1 0 _aNonparametric Bayesian Inference
_h[electronic resource] :
_bContributions by Jean-Marie Rolin /
_cedited by Jean-Pierre Florens, Michel Mouchart.
250 _a1st ed. 2024.
264 1 _aCham :
_bSpringer Nature Switzerland :
_bImprint: Springer,
_c2024.
300 _aXIII, 376 p. 17 illus.
_bonline resource.
336 _atext
_btxt
_2rdacontent
337 _acomputer
_bc
_2rdamedia
338 _aonline resource
_bcr
_2rdacarrier
347 _atext file
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505 0 _aChapter 1. On the a-Algebraic Realization Problem -- Chapter 2. Weak Conditional Independence And Relative Invariance in Bayesian Statistics -- Chapter 3. Some Useful Properties of the Dirichlet Process -- Chapter 4. On the Distribution of Jumps of the Dirichlet Process -- Chapter 5. Bayes, Bootstrap, Moments -- Chapter 6. Smooth vs. likelihood estimation for a class of mixtures of discrete distributions -- Chapter 7. Bayesian Encompassing Specification Tests of a Parametric Model against a Non Parametric Alternative -- Chapter 8. Nonparametric Bayesian Survival Analysis -- Chapter 9. Simulation of Posterior Distributions in Nonparametric Censored Analysis -- Chapter 10. Bayesian Identification of Semi-Parametric Binary Response Models -- Chapter 11. Survival Data with Explanatory Processes: A Full Nonparametric Bayesian Analysis -- Chapter 12. Nonparametric Competing Risks Models: Identification and Strong Consistency.
520 _aThis book is a compilation of unpublished papers written by Jean-Marie Rolin (with several co-authors) on nonparametric bayesian estimation. Jean-Marie was professor of statistics at University of Louvain and died on November 5th, 2018. He made important contributions in mathematical statistics with applications to different fields like econometrics or biometrics.These papers cover a variety of topics, including: • The Mathematical structure of the Bayesian model and the main concepts (sufficiency, ancillarity, invariance…) • Representation of the Dirichlet processes and of the associated Polya urn model and applications to nonparametric bayesian analysis. • Contributions to duration models and to their non parametric bayesian treatment.
650 0 _aStatistics .
_965982
650 1 4 _aStatistics.
_965982
650 2 4 _aBayesian Inference.
_976436
650 2 4 _aBayesian Network.
_979667
700 1 _aFlorens, Jean-Pierre.
_eeditor.
_4edt
_4http://id.loc.gov/vocabulary/relators/edt
_986369
700 1 _aMouchart, Michel.
_eeditor.
_4edt
_4http://id.loc.gov/vocabulary/relators/edt
_986370
710 2 _aSpringerLink (Online service)
773 0 _tSpringer Nature eBook
776 0 8 _iPrinted edition:
_z9783031613289
776 0 8 _iPrinted edition:
_z9783031613302
776 0 8 _iPrinted edition:
_z9783031613319
856 4 0 _uhttps://doi.org/10.1007/978-3-031-61329-6
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