We are analyzing https://link.springer.com/article/10.1007/s40300-025-00293-y.

Title:
Empirical Bayes methods in high dimensions: a survey and ongoing debates | METRON
Description:
Bayesian inference has as its starting point the specification of a prior distribution on the (possibly infinite-dimensional) parameters of the adopted statistical model. In some cases, a specification genuinely based on information available a priori and formalizing one’s level of uncertainty is difficult. This is especially true for the high-dimensional models in use for complex, recent applications of statistics and machine learning. In such circumstances, a popular practice, known as empirical Bayes, is to fix the value of the most relevant prior hyperparameters through the data. Notable examples are hyperparameters controlling sparsity in linear regression or sequence models; complexity in model selection or neural networks architecture; smoothness in nonparametric regression or density estimation. In spite of their popularity, empirical Bayesian methods still raise concerns of degeneracies and poor uncertainty quantification on the part of some scholars and practitioners, especially when set against fully Bayesian methods, whereby a hyperprior is specified on hyperparameters. The aim of this paper is to bring clarity by providing a critical review of recent advances in empirical Bayes methods for high-dimensional analysis. We offer an overview of their theoretical properties using the notion of oracle priors, illustrating with examples different facets of posterior adaptation. Finally, we discuss open issues, actively researched topics and future prospects.
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Keywords {🔍}

google, scholar, article, mathscinet, bayesian, stat, empirical, bayes, regression, priors, sparse, van, posterior, process, inference, highdimensional, learning, nonparametric, ann, gaussian, models, neural, electron, vaart, linear, learn, deep, data, methods, rizzelli, castillo, mach, res, horseshoe, analysis, machine, estimation, springer, szabó, cambridge, information, statistical, uncertainty, statistics, selection, arxiv, anal, processes, rousseau, privacy,

Topics {✒️}

month download article/chapter multivariate max-stable distributions tail-adaptive bayesian shrinkage high-dimensional linear regression bayesian model-based clustering slab posterior distributions infinite-dimensional statistical models unknown error variance high-dimensional analysis neural networks architecture practical bayesian optimization high-dimensional models discuss open issues high-dimensional properties sequence models adopted statistical model sparse linear models article metron aims full article pdf generalized additive models privacy choices/manage cookies possibly infinite-dimensional empirical bayes analysis accepted manuscript version bayesian credible sets high-dimensional statistics nonparametric bayesian inference gaussian process regression bayesian sensitivity analysis empirical bayes methods van der vaart van der wilk gaussian process priors empirical bayesian methods flexible statistical methods deep gaussian processes gaussian process prior empirical bayes inference fully bayesian methods related subjects bayesian linear regression nonparametric multivariate regression dirichlet process mixtures empirical bayes approach empirical bayes procedures empirical bayes estimates empirical bayes thresholding empirical bayes posteriors survival regression models sparse bayesian learning

Questions {❓}

Bayes and empirical Bayes: do they merge?
Can we trust Bayesian uncertainty quantification from Gaussian process priors with squared exponential covariance kernel?

Schema {🗺️}

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