Skip to search formSkip to main contentSkip to account menu

Conditions for Posterior Contraction in the Sparse Normal Means Problem

The first Bayesian results for the sparse normal means problem were proven for spike-and-slab priors. However, these priors are less convenient from a computational point of view. In the meanwhile, a…
View PDF on arXiv (opens in a new tab)

A general approach to posterior contraction in nonparametric inverse problems

In this paper we propose a general method to derive an upper bound for the contraction rate of the posterior distribution for nonparametric inverse problems. We present a general theorem that allows…
View PDF on arXiv (opens in a new tab)

Testing Un-Separated Hypotheses by Estimating a Distance

In this paper we propose a Bayesian answer to testing problems when the hypotheses are not well separated. The idea of the method is to study the posterior distribution of a discrepancy measure…
View PDF on arXiv (opens in a new tab)

Stratification of amyotrophic lateral sclerosis patients: a crowdsourcing approach

A new method to integrate and analyze patient clusters across methods is proposed, showing a clear pattern of consistent and clinically relevant sub-groups of patients that also enabled the reliable classification of new patients.
View on Springer (opens in a new tab)

Concentration rate and consistency of the posterior distribution for selected priors under monotonicity constraints

It is proved that the posterior distribution based on both priors concentrates at the rate (n/log(n))−1/3, which is the minimax rate of estimation up to a log(n) factor.
View PDF on arXiv (opens in a new tab)

Adaptive Bayes Test for Monotonicity

We study the asymptotic behavior of a Bayesian nonparametric test of qualitative hypotheses. More precisely, we focus on the problem of testing monotonicity of a regression function. Even if some…
View via Publisher (opens in a new tab)

Risk quantification for the thresholding rule for multiple testing using Gaussian scale mixtures

In this paper we study the asymptotic properties of Bayesian multiple testing procedures for a large class of Gaussian scale mixture pri- ors. We study two types of multiple testing risks: a Bayesian…
View PDF on arXiv (opens in a new tab)

Sequential Quasi Monte Carlo for Dirichlet Process Mixture Models

In mixture models, latent variables known as allocation variables play an essential role by indicating, at each iteration, to which component of the mixture observations are linked. In sequential…

Bayesian testing for embedded hypotheses with application to shape constrains

This paper proposes a general approach with a special focus on shape constrains testing and applies this method to several testing problems including testing for positivity and monotonicity in a nonparametric regression setting and shows that it leads to the optimal separation rate of testing.

Frequentist properties of Bayesian semiparametric and nonparametric procedures.

Research on Bayesian nonparametric methods has received a growing interest for the past twenty years, especially since the development of powerful simulation algorithms which makes the implementation…
By clicking accept or continuing to use the site, you agree to the terms outlined in our Privacy Policy (opens in a new tab), Terms of Service (opens in a new tab), and Dataset License (opens in a new tab)