Our work at IDSIA aims to adapt and extend Bayesian network to handle the heterogeneous, complex data that characterise application at the frontier of research. These include, for instance, handling big data in a computationally efficient way; taking into account the time and space dimensions; using incomplete data in effective ways; and combining sets of data collected under different experimental conditions. The focus is on providing production-ready software implementations of both industry-standard approaches and our own original methods for use in applications. Life and physical sciences require the complete interpretability that characterise Bayesian network over other machine learning models, and problems at the forefront of research in those disciplines routinely produce data like those described above.

References

• M. Scutari and J.-B. Denis (2021). Bayesian Networks with Examples in R. Chapman & Hall, 2nd edition.
• M. Scutari (2020). Bayesian Network Models for Incomplete and Dynamic Data. Statistica Neerlandica, 74(3), 397–419.
• M. Scutari, C. E. Graafland and J. M. Gutiérrez (2019). Who Learns Better Bayesian Network Structures: Accuracy and Speed of Structure Learning Algorithms. International Journal of Approximate Reasoning, 115, 235–253.
• M. Scutari, C. Vitolo and A. Tucker (2019). Learning Bayesian Networks from Big Data with Greedy Search: Computational Complexity and Efficient Implementation. Statistics and Computing, 29(5), 1095–1108.