Flexible estimation of temporal point processes and graphs

Thesis

Handling complex data types with spatial structures, temporal dependencies, or discrete values, is generally a challenge in statistics and machine learning. In the recent years, there has been an increasing need of methodological and theoretical work to analyse non-standard data types, for instance, data collected on protein structures, genes interactions, social networks or physical sensors. In this thesis, I will propose a methodology and provide theoretical guarantees for analysing two ...

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Authors: Sulem, D

• DOI: 10.5287/ora-bpxomok2k
• Type of award: DPhil
• Level of award: Doctoral
• Awarding institution: University of Oxford
• Language: English
• Keywords: Bayesian nonparametric inference; graph neural networks; spectral clustering
• Subjects: Bayesian statistics; Deep learning (Machine learning); Graph theory; Point processes; Mathematical statistics--Asymptotic theory