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Marco Zaffalon

On the complexity of solving polytree-shaped limited memory influence diagrams with binary variables
Evaluating credal classifiers by utility-discounted predictive accuracy
The Complexity of Approximately Solving Influence Diagrams
Updating Credal Networks is Approximable in Polynomial Time
Solving Limited Memory Influence Diagrams
A Fully Polynomial Time Approximation Scheme for Updating Credal Networks of Bounded Treewidth and Number of Variable States
Utility-Based Accuracy Measures to Empirically Evaluate Credal Classifiers

I thank the generous support of Alpha Vantage API in providing scholarly financial & statistical datasets used in our research

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