39 citations · 52 across the 5 of their papers we have counts for
5 papers
Variable Selection Using Nearest Neighbor Gaussian Processes
Konstantin Posch, Maximilian Arbeiter, Christian Truden +2
We introduce a novel Bayesian approach for variable selection using Gaussian process regression, which is crucial for enhancing interpretability and model regularization. Our metho…
A Bayesian Approach for Predicting Food and Beverage Sales in Staff Canteens and Restaurants
Konstantin Posch, Christian Truden, Philipp Hungerländer +1
Accurate demand forecasting is one of the key aspects for successfully managing restaurants and staff canteens. In particular, properly predicting future sales of menu items allows…
Correlated Parameters to Accurately Measure Uncertainty in Deep Neural Networks
Konstantin Posch, Jürgen Pilz
In this article a novel approach for training deep neural networks using Bayesian techniques is presented. The Bayesian methodology allows for an easy evaluation of model uncertain…
A novel Bayesian approach for variable selection in linear regression models
Konstantin Posch, Maximilian Arbeiter, Jürgen Pilz
We propose a novel Bayesian approach to the problem of variable selection in multiple linear regression models. In particular, we present a hierarchical setting which allows for di…
Variational Inference to Measure Model Uncertainty in Deep Neural Networks
Konstantin Posch, Jan Steinbrener, Jürgen Pilz
We present a novel approach for training deep neural networks in a Bayesian way. Classical, i.e. non-Bayesian, deep learning has two major drawbacks both originating from the fact…