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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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保存先:
フォーマット: Online
言語:英語
出版事項: MDPI - Multidisciplinary Digital Publishing Institute 2022
主題:
Berry–Esseen type theorem
Ewens–Pitman sampling model
exchangeable random partitions
log-series compound poisson sampling model
Mittag–Leffler distribution function
negative binomial compound poisson sampling model
Pitman’s α-diversity
wright distribution function
predictive distributions
random probability measures
reinforced processes
Pólya sequences
urn schemes
Bayesian inference
conditional identity in distribution
total variation distance
Bayesian nonparametrics
exchangeability
feature-sampling model
de Finetti theorem
Johnson’s “sufficientness” postulate
predictive distribution
scaled process prior
species-sampling model
Pólya urn
predictive mean
urn model
Wright—Fisher diffusion
species sampling models
exchangeable sequences
bayesian predictive inference
central limit theorem
stable convergence
best choice problem
optimal stopping time
last record
trapping strategy
algebraic statistics
contingency tables
de Finetti representation theorem
Markov basis
partial exchangeability
fragmentations of mass partitions
generalized gamma process
Mittag-Leffler Markov Chains
Poisson—Dirichlet distributions
species sampling
asymptotic efficiency
compatibility equations
decision theory
de Finetti’s representation theorem
Wasserstein distance
Fisher fiducial argument
inverse probability
uniform distribution
n/a
thema EDItEUR::G Reference, Information and Interdisciplinary subjects::GP Research and information: general
thema EDItEUR::P Mathematics and Science
オンライン･アクセス:ONIX_20221117_9783036551142_56
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要約: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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