On some recent advances on high dimensional Bayesian statistics

DOI10.1051/proc/201551016zbMath1330.62225OpenAlexW2099720176MaRDI QIDQ2786539

Benjamin Guedj, Nicolas Chopin, Sébastien Gadat, Arnaud Guyader, Elodie Vernet

Publication date: 15 February 2016

Published in: ESAIM: Proceedings and Surveys (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1051/proc/201551016

Mathematics Subject Classification ID

Estimation in multivariate analysis (62H12) Asymptotic properties of nonparametric inference (62G20) Bayesian inference (62F15) Markov processes: estimation; hidden Markov models (62M05)

Related Items (3)

User-friendly Introduction to PAC-Bayes Bounds ⋮ Bayesian Stochastic Gradient Descent for Stochastic Optimization with Streaming Input Data ⋮ Gibbs posterior convergence and the thermodynamic formalism

Uses Software

• pacbpred

Cites Work

• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• About the posterior distribution in hidden Markov models with unknown number of states
• Robust linear least squares regression
• PAC-Bayesian bounds for randomized empirical risk minimizers
• Asymptotic behavior of Bayes estimators for hidden Markov models with application to ion channels
• Concentration inequalities and model selection. Ecole d'Eté de Probabilités de Saint-Flour XXXIII -- 2003.
• On the consistency of Bayes estimates
• Additive regression and other nonparametric models
• Generalized additive models
• I-divergence geometry of probability distributions and minimization problems
• Space-filling curves
• Asymptotics in statistics. Some basic concepts.
• Asymptotic properties of the maximum likelihood estimator in autoregressive models with Markov regime
• Convergence rates of posterior distributions.
• Statistical learning theory and stochastic optimization. Ecole d'Eté de Probabilitiés de Saint-Flour XXXI -- 2001.
• PAC-Bayesian estimation and prediction in sparse additive models
• PAC-Bayesian bounds for sparse regression estimation with exponential weights
• Posterior consistency for nonparametric hidden Markov models with finite state space
• Consistency of the maximum likelihood estimator for general hidden Markov models
• Monte Carlo and quasi-Monte Carlo sampling
• Maximum Likelihood Estimation for Spatial Models by Markov Chain Monte Carlo Stochastic Approximation
• PAC-Bayesian Inequalities for Martingales
• PAC-Bayesian Generalisation Error Bounds for Gaussian Process Classification
• Information Theory and Mixing Least-Squares Regressions
• A theory of the learnable
• Necessary and sufficient conditions for the pointwise convergence of nearest neighbor regression function estimates
• A Nonparametric Estimate of a Multivariate Density Function
• Sequential Monte Carlo without likelihoods
• Sparse single-index model
• Prediction of time series by statistical learning: general losses and fast rates
• Nearest neighbor classification in infinite dimension
• Estimation by the nearest neighbor rule
• Monte Carlo sampling methods using Markov chains and their applications
• Asymptotics of the maximum likelihood estimator for general hidden Markov models

This page was built for publication: On some recent advances on high dimensional Bayesian statistics