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Structure learning for CTBN's via penalized maximum likelihood methods
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 occurring in living processes, in social science models or in medicine.
Analysis of Langevin Monte Carlo via convex optimization
In this paper, we provide new insights on the Unadjusted Langevin Algorithm.
Particle MCMC with Poisson Resampling: Parallelization and Continuous Time Models
We introduce a new version of particle filter in which the number of "children" of a particle at a given time has a Poisson distribution.
Computation
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