Bayesian Predictive Inference and Related Asymptotics-Festschrift for Eugenio Regazzini's 75th Birthday
Bayesian predictive inference is at the core of the mathematical theory of inductive reasoning. Nowadays, this field has become very attractive especially for its connections with algorithmic probability, machine learning and artificial intelligence. The complexity of both problems and algorithm rep...
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| Format: | Electronic Book |
| Language: | English |
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Basel
MDPI - Multidisciplinary Digital Publishing Institute
2022
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| Online Access: | DOAB: download the publication DOAB: description of the publication |
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| 072 | 7 | |a GP |2 bicssc | |
| 072 | 7 | |a P |2 bicssc | |
| 100 | 1 | |a Dolera, Emanuele |4 edt | |
| 700 | 1 | |a Bassetti, Federico |4 edt | |
| 700 | 1 | |a Dolera, Emanuele |4 oth | |
| 700 | 1 | |a Bassetti, Federico |4 oth | |
| 245 | 1 | 0 | |a Bayesian Predictive Inference and Related Asymptotics-Festschrift for Eugenio Regazzini's 75th Birthday |
| 260 | |a Basel |b MDPI - Multidisciplinary Digital Publishing Institute |c 2022 | ||
| 300 | |a 1 electronic resource (198 p.) | ||
| 336 | |a text |b txt |2 rdacontent | ||
| 337 | |a computer |b c |2 rdamedia | ||
| 338 | |a online resource |b cr |2 rdacarrier | ||
| 506 | 0 | |a Open Access |2 star |f Unrestricted online access | |
| 520 | |a Bayesian predictive inference is at the core of the mathematical theory of inductive reasoning. Nowadays, this field has become very attractive especially for its connections with algorithmic probability, machine learning and artificial intelligence. The complexity of both problems and algorithm represents a constant source of research of asymptotic techniques, which are necessary to handle vast datasets. The present book contains the 11 papers accepted and published in the Special Issue "Bayesian Predictive Inference and Related Asymptotics-Festschrift for Eugenio Regazzini's 75th Birthday" of the MDPI Mathematics journal. The topics of the paper focus, among others, on Bayesian nonparametrics, species sampling models, partial exchangeability and optimal stopping. Finally, as the title suggests, the Special Issue aims to celebrate the 75th birthday of Prof. Eugenio Regazzini, who has provided so many important contributions to the field of Bayesian inference. | ||
| 540 | |a Creative Commons |f https://creativecommons.org/licenses/by/4.0/ |2 cc |4 https://creativecommons.org/licenses/by/4.0/ | ||
| 546 | |a English | ||
| 650 | 7 | |a Research & information: general |2 bicssc | |
| 650 | 7 | |a Mathematics & science |2 bicssc | |
| 653 | |a Berry-Esseen type theorem | ||
| 653 | |a Ewens-Pitman sampling model | ||
| 653 | |a exchangeable random partitions | ||
| 653 | |a log-series compound poisson sampling model | ||
| 653 | |a Mittag-Leffler distribution function | ||
| 653 | |a negative binomial compound poisson sampling model | ||
| 653 | |a Pitman's α-diversity | ||
| 653 | |a wright distribution function | ||
| 653 | |a predictive distributions | ||
| 653 | |a random probability measures | ||
| 653 | |a reinforced processes | ||
| 653 | |a Pólya sequences | ||
| 653 | |a urn schemes | ||
| 653 | |a Bayesian inference | ||
| 653 | |a conditional identity in distribution | ||
| 653 | |a total variation distance | ||
| 653 | |a Bayesian nonparametrics | ||
| 653 | |a exchangeability | ||
| 653 | |a feature-sampling model | ||
| 653 | |a de Finetti theorem | ||
| 653 | |a Johnson's "sufficientness" postulate | ||
| 653 | |a predictive distribution | ||
| 653 | |a scaled process prior | ||
| 653 | |a species-sampling model | ||
| 653 | |a Pólya urn | ||
| 653 | |a predictive mean | ||
| 653 | |a urn model | ||
| 653 | |a Wright-Fisher diffusion | ||
| 653 | |a species sampling models | ||
| 653 | |a exchangeable sequences | ||
| 653 | |a bayesian predictive inference | ||
| 653 | |a central limit theorem | ||
| 653 | |a stable convergence | ||
| 653 | |a best choice problem | ||
| 653 | |a optimal stopping time | ||
| 653 | |a last record | ||
| 653 | |a trapping strategy | ||
| 653 | |a algebraic statistics | ||
| 653 | |a contingency tables | ||
| 653 | |a de Finetti representation theorem | ||
| 653 | |a Markov basis | ||
| 653 | |a partial exchangeability | ||
| 653 | |a fragmentations of mass partitions | ||
| 653 | |a generalized gamma process | ||
| 653 | |a Mittag-Leffler Markov Chains | ||
| 653 | |a Poisson-Dirichlet distributions | ||
| 653 | |a species sampling | ||
| 653 | |a asymptotic efficiency | ||
| 653 | |a compatibility equations | ||
| 653 | |a decision theory | ||
| 653 | |a de Finetti's representation theorem | ||
| 653 | |a Wasserstein distance | ||
| 653 | |a Fisher fiducial argument | ||
| 653 | |a inverse probability | ||
| 653 | |a uniform distribution | ||
| 653 | |a n/a | ||
| 856 | 4 | 0 | |a www.oapen.org |u https://mdpi.com/books/pdfview/book/6228 |7 0 |z DOAB: download the publication |
| 856 | 4 | 0 | |a www.oapen.org |u https://directory.doabooks.org/handle/20.500.12854/93799 |7 0 |z DOAB: description of the publication |