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stat.ML2023
Structure Learning and Parameter Estimation for Graphical Models via Penalized Maximum Likelihood Methods
Maryia Shpak
Probabilistic graphical models (PGMs) provide a compact and flexible framework to model very complex real-life phenomena. They combine the probability theory which deals with uncer…
stat.ML2020
Structure learning for CTBN's via penalized maximum likelihood methods
Maryia Shpak, Błażej Miasojedow, Wojciech Rejchel
The continuous-time Bayesian networks (CTBNs) represent a class of stochastic processes, which can be used to model complex phenomena, for instance, they can describe interactions…