research
∙
05/09/2023
Electricity Load and Peak Forecasting: Feature Engineering, Probabilistic LightGBM and Temporal Hierarchies
We describe our experience in developing a predictive model that placed ...
share
research
∙
03/27/2023
Properties of the reconciled distributions for Gaussian and count forecasts
Reconciliation enforces coherence between hierarchical forecasts, in ord...
research
∙
10/05/2022
Probabilistic reconciliation of forecasts via importance sampling
Hierarchical time series are common in several applied fields. Forecasts...
research
∙
07/19/2022
Probabilistic Reconciliation of Count Time Series
We propose a principled method for the reconciliation of any probabilist...
research
∙
09/17/2020
Automatic Forecasting using Gaussian Processes
Automatic forecasting is the task of receiving a time series and returni...
research
∙
08/04/2020
Structure Learning from Related Data Sets with a Hierarchical Bayesian Score
Score functions for learning the structure of Bayesian networks in the l...
research
∙
06/07/2019
Reconciling Hierarchical Forecasts via Bayes' Rule
When time series are organized into hierarchies, the forecasts have to s...
research
∙
02/07/2018
Efficient Learning of Bounded-Treewidth Bayesian Networks from Complete and Incomplete Data Sets
Learning a Bayesian networks with bounded treewidth is important for red...
research
∙
07/19/2017
Entropy-based Pruning for Learning Bayesian Networks using BIC
For decomposable score-based structure learning of Bayesian networks, ex...
research
∙
09/28/2016
Statistical comparison of classifiers through Bayesian hierarchical modelling
Usually one compares the accuracy of two competing classifiers via null ...
research
∙
06/14/2016
Time for a change: a tutorial for comparing multiple classifiers through Bayesian analysis
The machine learning community adopted the use of null hypothesis signif...
research
∙
05/09/2015
Should we really use post-hoc tests based on mean-ranks?
The statistical comparison of multiple algorithms over multiple data set...
research
∙
05/14/2014
Credal Model Averaging for classification: representing prior ignorance and expert opinions
Bayesian model averaging (BMA) is the state of the art approach for over...
research
∙
10/12/2011
