Thompson sampling (TS) is one of the most popular and earliest algorithm...
In a traditional Gaussian graphical model, data homogeneity is routinely...
We study Bayesian histograms for distribution estimation on [0,1]^d unde...
As a computational alternative to Markov chain Monte Carlo approaches,
v...
The advent of ML-driven decision-making and policy formation has led to ...
Posterior computation in hierarchical Dirichlet process (HDP) mixture mo...
Flexible Bayesian models are typically constructed using limits of large...
Gaussian graphical models typically assume a homogeneous structure acros...
Modern data science applications often involve complex relational data w...
We consider a latent space model for dynamic networks, where our objecti...
We consider the off-policy evaluation (OPE) problem in contextual bandit...
We develop a framework to study posterior contraction rates in sparse hi...
Computing the marginal likelihood or evidence is one of the core challen...
A systematic approach to finding variational approximation in an otherwi...
Transformation-based methods have been an attractive approach in
non-par...
We provide statistical guarantees for Bayesian variational boosting by
p...
The marginal likelihood or evidence in Bayesian statistics contains an
i...
Variational algorithms have gained prominence over the past two decades ...
Modern genomic studies are increasingly focused on identifying more and ...
We present non-asymptotic two-sided bounds to the log-marginal likelihoo...
We show that any lower-dimensional marginal density obtained from trunca...
Estimating the marginal and joint densities of the long-term average int...
Covariate measurement error in nonparametric regression is a common prob...
This article revisits the problem of Bayesian shape-restricted inference...
Gaussian graphical models are a popular tool to learn the dependence
str...
Analysis of structural and functional connectivity (FC) of human brains ...
Background: The "proton radius puzzle" refers to an eight-year old probl...
We propose a new distribution, called the soft tMVN distribution, which
...
While the problem of estimating a probability density function (pdf) fro...
The article addresses a long-standing open problem on the justification ...
We propose a variational approximation to Bayesian posterior distributio...
Gaussian process (GP) regression is a powerful interpolation technique d...
In this article, we investigate large sample properties of model selecti...
Additive nonparametric regression models provide an attractive tool for
...
Unsupervised clustering of curves according to their shapes is an import...