Projective inference in high-dimensional problems: Prediction and feature selection
J Piironen, M Paasiniemi, A Vehtari - 2020 - projecteuclid.org
This paper reviews predictive inference and feature selection for generalized linear models
with scarce but high-dimensional data. We demonstrate that in many cases one can benefit …
with scarce but high-dimensional data. We demonstrate that in many cases one can benefit …
Penalising model component complexity: A principled, practical approach to constructing priors
Supplement to “Penalising Model Component Complexity: A Principled, Practical Approach
to Constructing Priors”. The supplementary material contains the proofs of all theorems …
to Constructing Priors”. The supplementary material contains the proofs of all theorems …
Sparsity information and regularization in the horseshoe and other shrinkage priors
J Piironen, A Vehtari - 2017 - projecteuclid.org
The horseshoe prior has proven to be a noteworthy alternative for sparse Bayesian
estimation, but has previously suffered from two problems. First, there has been no …
estimation, but has previously suffered from two problems. First, there has been no …
The spike-and-slab lasso
Despite the wide adoption of spike-and-slab methodology for Bayesian variable selection,
its potential for penalized likelihood estimation has largely been overlooked. In this article …
its potential for penalized likelihood estimation has largely been overlooked. In this article …
Prior knowledge elicitation: The past, present, and future
Prior Knowledge Elicitation: The Past, Present, and Future Page 1 Bayesian Analysis (2024)
19, Number 4, pp. 1129–1161 Prior Knowledge Elicitation: The Past, Present, and Future ∗ …
19, Number 4, pp. 1129–1161 Prior Knowledge Elicitation: The Past, Present, and Future ∗ …
Spike-and-slab meets LASSO: A review of the spike-and-slab LASSO
High-dimensional data sets have become ubiquitous in the past few decades, often with
many more covariates than observations. In the frequentist setting, penalized likelihood …
many more covariates than observations. In the frequentist setting, penalized likelihood …
Sparsifying priors for Bayesian uncertainty quantification in model discovery
SM Hirsh, DA Barajas-Solano… - Royal Society Open …, 2022 - royalsocietypublishing.org
We propose a probabilistic model discovery method for identifying ordinary differential
equations governing the dynamics of observed multivariate data. Our method is based on …
equations governing the dynamics of observed multivariate data. Our method is based on …
Bayesian regression trees for high-dimensional prediction and variable selection
AR Linero - Journal of the American Statistical Association, 2018 - Taylor & Francis
Decision tree ensembles are an extremely popular tool for obtaining high-quality predictions
in nonparametric regression problems. Unmodified, however, many commonly used …
in nonparametric regression problems. Unmodified, however, many commonly used …
Prior distributions for objective Bayesian analysis
We provide a review of prior distributions for objective Bayesian analysis. We start by
examining some foundational issues and then organize our exposition into priors for: i) …
examining some foundational issues and then organize our exposition into priors for: i) …
Fast sampling with Gaussian scale mixture priors in high-dimensional regression
We propose an efficient way to sample from a class of structured multivariate Gaussian
distributions. The proposed algorithm only requires matrix multiplications and linear system …
distributions. The proposed algorithm only requires matrix multiplications and linear system …