research
∙
09/05/2023
Adaptive Bayesian Predictive Inference
Bayesian predictive inference provides a coherent description of entire ...
share
research
∙
05/31/2023
On Mixing Rates for Bayesian CART
The success of Bayesian inference with MCMC depends critically on Markov...
research
∙
08/25/2022
Adversarial Bayesian Simulation
In the absence of explicit or tractable likelihoods, Bayesians often res...
research
∙
05/30/2022
Deep Bootstrap for Bayesian Inference
For a Bayesian, the task to define the likelihood can be as perplexing a...
research
∙
11/22/2021
Approximate Bayesian Computation via Classification
Approximate Bayesian Computation (ABC) enables statistical inference in ...
research
∙
05/26/2021
Ideal Bayesian Spatial Adaptation
Many real-life applications involve estimation of curves that exhibit co...
research
∙
03/06/2021
Metropolis-Hastings via Classification
This paper develops a Bayesian computational platform at the interface b...
research
∙
11/29/2020
Bayesian Bootstrap Spike-and-Slab LASSO
The impracticality of posterior sampling has prevented the widespread ad...
research
∙
10/13/2020
Spike-and-Slab Meets LASSO: A Review of the Spike-and-Slab LASSO
High-dimensional data sets have become ubiquitous in the past few decade...
research
∙
08/15/2020
The art of BART: On flexibility of Bayesian forests
Considerable effort has been directed to developing asymptotically minim...
research
∙
07/01/2020
Variable Selection via Thompson Sampling
Thompson sampling is a heuristic algorithm for the multi-armed bandit pr...
research
∙
02/26/2020
Uncertainty Quantification for Sparse Deep Learning
Deep learning methods continue to have a decided impact on machine learn...
research
∙
10/16/2019
Multiscale Analysis of Bayesian CART
This paper affords new insights about Bayesian CART in the context of st...
research
∙
09/14/2019
Adaptive Bayesian SLOPE – High-dimensional Model Selection with Missing Values
The selection of variables with high-dimensional and missing data is a m...
research
∙
05/09/2019
On Semi-parametric Bernstein-von Mises Theorems for BART
Few methods in Bayesian non-parametric statistics/ machine learning have...
research
∙
12/11/2018
Dynamic Sparse Factor Analysis
Its conceptual appeal and effectiveness has made latent factor modeling ...
research
∙
10/01/2018
On Theory for BART
Ensemble learning is a statistical paradigm built on the premise that ma...
research
∙
07/22/2018
The Median Probability Model and Correlated Variables
The median probability model (MPM) Barbieri and Berger (2004) is defined...
research
∙
06/06/2018
ABC Variable Selection with Bayesian Forests
Few problems in statistics are as perplexing as variable selection in th...
research
∙
03/24/2018
Posterior Concentration for Sparse Deep Learning
Spike-and-Slab Deep Learning (SS-DL) is a fully Bayesian alternative to ...
research
∙
01/09/2018
