2 citations · 2 across the 4 of their papers we have counts for
5 papers
Hierarchical Forecast Reconciliation on Networks: A Network Flow Optimization Formulation
Charupriya Sharma, Iñaki Estella Aguerri, Daniel Guimarans
Hierarchical forecasting with reconciliation requires forecasting values of a hierarchy (e.g.~customer demand in a state and district), such that forecast values are linked (e.g.~…
Scalable Bayesian Network Structure Learning with Splines
Charupriya Sharma, Peter van Beek
The graph structure of a Bayesian network (BN) can be learned from data using the well-known score-and-search approach. Previous work has shown that incorporating structured repres…
A Score-and-Search Approach to Learning Bayesian Networks with Noisy-OR Relations
Charupriya Sharma, Zhenyu A. Liao, James Cussens +1
A Bayesian network is a probabilistic graphical model that consists of a directed acyclic graph (DAG), where each node is a random variable and attached to each node is a condition…
Learning All Credible Bayesian Network Structures for Model Averaging
Zhenyu A. Liao, Charupriya Sharma, James Cussens +1
A Bayesian network is a widely used probabilistic graphical model with applications in knowledge discovery and prediction. Learning a Bayesian network (BN) from data can be cast as…
Finding All Bayesian Network Structures within a Factor of Optimal
Zhenyu A. Liao, Charupriya Sharma, James Cussens +1
A Bayesian network is a widely used probabilistic graphical model with applications in knowledge discovery and prediction. Learning a Bayesian network (BN) from data can be cast as…