484 citations · 1.1k across the 13 of their papers we have counts for
13 papers
Using reference models in variable selection
Federico Pavone, Juho Piironen, Paul-Christian Bürkner +1
Variable selection, or more generally, model reduction is an important aspect of the statistical workflow aiming to provide insights from data. In this paper, we discuss and demons…
A Decision-Theoretic Approach for Model Interpretability in Bayesian Framework
Homayun Afrabandpey, Tomi Peltola, Juho Piironen +2
A salient approach to interpretable machine learning is to restrict modeling to simple models. In the Bayesian framework, this can be pursued by restricting the model structure and…
Implicitly Adaptive Importance Sampling
Topi Paananen, Juho Piironen, Paul-Christian Bürkner +1
Adaptive importance sampling is a class of techniques for finding good proposal distributions for importance sampling. Often the proposal distributions are standard probability dis…
Projective Inference in High-dimensional Problems: Prediction and Feature Selection
Juho Piironen, Markus Paasiniemi, Aki Vehtari
This paper discusses predictive inference and feature selection for generalized linear models with scarce but high-dimensional data. We argue that in many cases one can benefit fro…
Bayesian Estimation of Gaussian Graphical Models with Predictive Covariance Selection
Donald R. Williams, Juho Piironen, Aki Vehtari +1
Gaussian graphical models are used for determining conditional relationships between variables. This is accomplished by identifying off-diagonal elements in the inverse-covariance…
Variable selection for Gaussian processes via sensitivity analysis of the posterior predictive distribution
Topi Paananen, Juho Piironen, Michael Riis Andersen +1
Variable selection for Gaussian process models is often done using automatic relevance determination, which uses the inverse length-scale parameter of each input variable as a prox…
Iterative Supervised Principal Components
Juho Piironen, Aki Vehtari
In high-dimensional prediction problems, where the number of features may greatly exceed the number of training instances, fully Bayesian approach with a sparsifying prior is known…
Sparsity information and regularization in the horseshoe and other shrinkage priors
Juho Piironen, Aki Vehtari
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 systematic way…
Automatic monotonicity detection for Gaussian Processes
Eero Siivola, Juho Piironen, Aki Vehtari
We propose a new method for automatically detecting monotonic input-output relationships from data using Gaussian Process (GP) models with virtual derivative observations. Our resu…
On the Hyperprior Choice for the Global Shrinkage Parameter in the Horseshoe Prior
Juho Piironen, Aki Vehtari
The horseshoe prior has proven to be a noteworthy alternative for sparse Bayesian estimation, but as shown in this paper, the results can be sensitive to the prior choice for the g…
Projection predictive model selection for Gaussian processes
Juho Piironen, Aki Vehtari
We propose a new method for simplification of Gaussian process (GP) models by projecting the information contained in the full encompassing model and selecting a reduced number of…
Projection predictive variable selection using Stan+R
Juho Piironen, Aki Vehtari
This document is additional material to our previous study comparing several strategies for variable subset selection. Our recommended approach was to fit the full model with all t…
Comparison of Bayesian predictive methods for model selection
Juho Piironen, Aki Vehtari
The goal of this paper is to compare several widely used Bayesian model selection methods in practical model selection problems, highlight their differences and give recommendation…