124 citations · 588 across the 20 of their papers we have counts for
8 papers · 1 filter
Investigating the performance of multi-objective optimization when learning Bayesian Networks
Paolo Cazzaniga, Marco S. Nobile, Daniele Ramazzotti
Bayesian Networks have been widely used in the last decades in many fields, to describe statistical dependencies among random variables. In general, learning the structure of such…
Learning the structure of Bayesian Networks via the bootstrap
Giulio Caravagna, Daniele Ramazzotti
Learning the structure of dependencies among multiple random variables is a problem of considerable theoretical and practical interest. Within the context of Bayesian Networks, a p…
Learning the structure of Bayesian Networks: A quantitative assessment of the effect of different algorithmic schemes
Stefano Beretta, Mauro Castelli, Ivo Goncalves +2
One of the most challenging tasks when adopting Bayesian Networks (BNs) is the one of learning their structure from data. This task is complicated by the huge search space of possi…
Combining Bayesian Approaches and Evolutionary Techniques for the Inference of Breast Cancer Networks
Stefano Beretta, Mauro Castelli, Ivo Goncalves +2
Gene and protein networks are very important to model complex large-scale systems in molecular biology. Inferring or reverseengineering such networks can be defined as the process…
Parallel Implementation of Efficient Search Schemes for the Inference of Cancer Progression Models
Daniele Ramazzotti, Marco S. Nobile, Paolo Cazzaniga +2
The emergence and development of cancer is a consequence of the accumulation over time of genomic mutations involving a specific set of genes, which provides the cancer clones with…
Causal Data Science for Financial Stress Testing
Gelin Gao, Bud Mishra, Daniele Ramazzotti
The most recent financial upheavals have cast doubt on the adequacy of some of the conventional quantitative risk management strategies, such as VaR (Value at Risk), in many common…
Efficient computational strategies to learn the structure of probabilistic graphical models of cumulative phenomena
Daniele Ramazzotti, Marco S. Nobile, Marco Antoniotti +1
Structural learning of Bayesian Networks (BNs) is a NP-hard problem, which is further complicated by many theoretical issues, such as the I-equivalence among different structures.…
A Model of Selective Advantage for the Efficient Inference of Cancer Clonal Evolution
Daniele Ramazzotti
Recently, there has been a resurgence of interest in rigorous algorithms for the inference of cancer progression from genomic data. The motivations are manifold: (i) growing NGS an…