Veronika Rockova | DeepAI

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Błażej Miasojedow
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∙ 09/05/2023

Adaptive Bayesian Predictive Inference

Bayesian predictive inference provides a coherent description of entire ...

0 Veronika Rockova, et al. ∙

∙ 05/31/2023

On Mixing Rates for Bayesian CART

The success of Bayesian inference with MCMC depends critically on Markov...

0 Jungeum Kim, et al. ∙

∙ 08/25/2022

Adversarial Bayesian Simulation

In the absence of explicit or tractable likelihoods, Bayesians often res...

0 Yuexi Wang, et al. ∙

∙ 05/30/2022

Deep Bootstrap for Bayesian Inference

For a Bayesian, the task to define the likelihood can be as perplexing a...

0 Lizhen Nie, et al. ∙

∙ 11/22/2021

Approximate Bayesian Computation via Classification

Approximate Bayesian Computation (ABC) enables statistical inference in ...

0 Yuexi Wang, et al. ∙

∙ 05/26/2021

Ideal Bayesian Spatial Adaptation

Many real-life applications involve estimation of curves that exhibit co...

0 Veronika Rockova, et al. ∙

∙ 03/06/2021

Metropolis-Hastings via Classification

This paper develops a Bayesian computational platform at the interface b...

0 Tetsuya Kaji, et al. ∙

∙ 11/29/2020

Bayesian Bootstrap Spike-and-Slab LASSO

The impracticality of posterior sampling has prevented the widespread ad...

0 Lizhen Nie, et al. ∙

∙ 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...

0 Ray Bai, et al. ∙

∙ 08/15/2020

The art of BART: On flexibility of Bayesian forests

Considerable effort has been directed to developing asymptotically minim...

0 Seonghyun Jeong, et al. ∙

∙ 07/01/2020

Variable Selection via Thompson Sampling

Thompson sampling is a heuristic algorithm for the multi-armed bandit pr...

0 YI LIU, et al. ∙

∙ 02/26/2020

Uncertainty Quantification for Sparse Deep Learning

Deep learning methods continue to have a decided impact on machine learn...

0 Yuexi Wang, et al. ∙

∙ 10/16/2019

Multiscale Analysis of Bayesian CART

This paper affords new insights about Bayesian CART in the context of st...

0 Ismaël Castillo, et al. ∙

∙ 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...

0 Wei Jiang, et al. ∙

∙ 05/09/2019

On Semi-parametric Bernstein-von Mises Theorems for BART

Few methods in Bayesian non-parametric statistics/ machine learning have...

0 Veronika Rockova, et al. ∙

∙ 12/11/2018

Dynamic Sparse Factor Analysis

Its conceptual appeal and effectiveness has made latent factor modeling ...

0 Kenichiro McAlinn, et al. ∙

∙ 10/01/2018

On Theory for BART

Ensemble learning is a statistical paradigm built on the premise that ma...

0 Veronika Rockova, et al. ∙

∙ 07/22/2018

The Median Probability Model and Correlated Variables

The median probability model (MPM) Barbieri and Berger (2004) is defined...

0 Marilena Barbieri, et al. ∙

∙ 06/06/2018

ABC Variable Selection with Bayesian Forests

Few problems in statistics are as perplexing as variable selection in th...

0 YI LIU, et al. ∙

∙ 03/24/2018

Posterior Concentration for Sparse Deep Learning

Spike-and-Slab Deep Learning (SS-DL) is a fully Bayesian alternative to ...

0 Nicholas Polson, et al. ∙

∙ 01/09/2018

On variance estimation for Bayesian variable selection

Consider the problem of high dimensional variable selection for the Gaus...

0 Gemma E. Moran, et al. ∙

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