Adaptive variational Bayes: optimality, computation and applications
From MaRDI portal
Publication:6192331
DOI10.1214/23-aos2349arXiv2109.03204OpenAlexW4392591484MaRDI QIDQ6192331
Publication date: 11 March 2024
Published in: The Annals of Statistics (Search for Journal in Brave)
Full work available at URL: https://arxiv.org/abs/2109.03204
variational Bayesmodel selection consistencyadaptive inferencedeep neural networksposterior contraction ratesquasi-posteriors
Asymptotic properties of nonparametric inference (62G20) Bayesian problems; characterization of Bayes procedures (62C10)
Cites Work
- Unnamed Item
- From \(\varepsilon\)-entropy to KL-entropy: analysis of minimum information complexity density estima\-tion
- Bayesian linear regression with sparse priors
- Convergence rates of posterior distributions for non iid observations
- Adaptive Bayesian inference on the mean of an infinite-dimensional normal distribution
- Bayesian fractional posteriors
- Consistency of variational Bayes inference for estimation and model selection in mixtures
- Optimal global rates of convergence for nonparametric regression
- Convergence rates of posterior distributions.
- Asymptotic behaviour of the empirical Bayes posteriors associated to maximum marginal likelihood estimator
- Oracle posterior contraction rates under hierarchical priors
- On the rate of convergence of fully connected deep neural network regression estimates
- Variable selection consistency of Gaussian process regression
- Bayesian high-dimensional semi-parametric inference beyond sub-Gaussian errors
- \(\alpha\)-variational inference with statistical guarantees
- Concentration of tempered posteriors and of their variational approximations
- Nonparametric regression using deep neural networks with ReLU activation function
- Convergence rates of variational posterior distributions
- Theoretical and computational guarantees of mean field variational inference for community detection
- A general framework for Bayes structured linear models
- Optimal estimation and rank detection for sparse spiked covariance matrices
- Minimax-optimal nonparametric regression in high dimensions
- Nonparametric Bayesian model selection and averaging
- Posterior contraction in sparse Bayesian factor models for massive covariance matrices
- Gibbs posterior concentration rates under sub-exponential type losses
- Optimal Bayesian estimation of Gaussian mixtures with growing number of components
- On Bayesian Consistency
- Bayesian Optimal Adaptive Estimation Using a Sieve Prior
- On the properties of variational approximations of Gibbs posteriors
- Sparse Bayesian Methods for Low-Rank Matrix Estimation
- Mixture Models With a Prior on the Number of Components
- Bayesian sparse linear regression with unknown symmetric error
- Probabilistic Community Detection With Unknown Number of Communities
- On universal Bayesian adaptation
- Variational Bayes for High-Dimensional Linear Regression With Sparse Priors
