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: eBook
Language:English
Published: Basel MDPI - Multidisciplinary Digital Publishing Institute 2022
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Online Access:Open Access: DOAB: description of the publication
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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. 
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650 7 |a Mathematics & science  |2 bicssc 
650 7 |a Research & information: general  |2 bicssc 
653 |a algebraic statistics 
653 |a asymptotic efficiency 
653 |a Bayesian inference 
653 |a Bayesian nonparametrics 
653 |a bayesian predictive inference 
653 |a Berry-Esseen type theorem 
653 |a best choice problem 
653 |a central limit theorem 
653 |a compatibility equations 
653 |a conditional identity in distribution 
653 |a contingency tables 
653 |a de Finetti representation theorem 
653 |a de Finetti theorem 
653 |a de Finetti's representation theorem 
653 |a decision theory 
653 |a Ewens-Pitman sampling model 
653 |a exchangeability 
653 |a exchangeable random partitions 
653 |a exchangeable sequences 
653 |a feature-sampling model 
653 |a Fisher fiducial argument 
653 |a fragmentations of mass partitions 
653 |a generalized gamma process 
653 |a inverse probability 
653 |a Johnson's "sufficientness" postulate 
653 |a last record 
653 |a log-series compound poisson sampling model 
653 |a Markov basis 
653 |a Mittag-Leffler distribution function 
653 |a Mittag-Leffler Markov Chains 
653 |a n/a 
653 |a negative binomial compound poisson sampling model 
653 |a optimal stopping time 
653 |a partial exchangeability 
653 |a Pitman's α-diversity 
653 |a Poisson-Dirichlet distributions 
653 |a Pólya sequences 
653 |a Pólya urn 
653 |a predictive distribution 
653 |a predictive distributions 
653 |a predictive mean 
653 |a random probability measures 
653 |a reinforced processes 
653 |a scaled process prior 
653 |a species sampling 
653 |a species sampling models 
653 |a species-sampling model 
653 |a stable convergence 
653 |a total variation distance 
653 |a trapping strategy 
653 |a uniform distribution 
653 |a urn model 
653 |a urn schemes 
653 |a Wasserstein distance 
653 |a wright distribution function 
653 |a Wright-Fisher diffusion 
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856 4 0 |u https://mdpi.com/books/pdfview/book/6228  |7 0  |z Open Access: DOAB, download the publication