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Found 13 papers, 3 papers with code
Efficient probabilistic reconciliation of forecasts for real-valued and count time series
The forecasts for these time series are required to be coherent, that is, to satisfy the constraints given by the hierarchy.
Probabilistic Reconciliation of Count Time Series
Forecast reconciliation is an important research topic.
Time series forecasting with Gaussian Processes needs priors
Automatic forecasting is the task of receiving a time series and returning a forecast for the next time steps without any human intervention.
A Bayesian Hierarchical Score for Structure Learning from Related Data Sets
In this paper we propose a new Bayesian Dirichlet score, which we call Bayesian Hierarchical Dirichlet (BHD).
Efficient Learning of Bounded-Treewidth Bayesian Networks from Complete and Incomplete Data Sets
We present a novel anytime algorithm (k-MAX) method for this task, which scales up to thousands of variables.
Entropy-based Pruning for Learning Bayesian Networks using BIC
For decomposable score-based structure learning of Bayesian networks, existing approaches first compute a collection of candidate parent sets for each variable and then optimize over this collection by choosing one parent set for each variable without creating directed cycles while maximizing the total score.
Learning Treewidth-Bounded Bayesian Networks with Thousands of Variables
We present a method for learning treewidth-bounded Bayesian networks from data sets containing thousands of variables.
Statistical comparison of classifiers through Bayesian hierarchical modelling
Usually one compares the accuracy of two competing classifiers via null hypothesis significance tests (nhst).
Time for a change: a tutorial for comparing multiple classifiers through Bayesian analysis
The machine learning community adopted the use of null hypothesis significance testing (NHST) in order to ensure the statistical validity of results.
Learning Bounded Treewidth Bayesian Networks with Thousands of Variables
We present a method for learning treewidth-bounded Bayesian networks from data sets containing thousands of variables.
Learning Bayesian Networks with Thousands of Variables
We present a method for learning Bayesian networks from data sets containingthousands of variables without the need for structure constraints.
Should we really use post-hoc tests based on mean-ranks?
In other words, the outcome of the comparison between algorithms A and B depends also on the performance of the other algorithms included in the original experiment.
Credal Model Averaging for classification: representing prior ignorance and expert opinions
CMA detects prior-dependent instances, namely instances in which a different class is more probable depending on the prior over the models.
