7 papers
Properties and limitations of geometric tempering for gradient flow dynamics
Francesca Romana Crucinio, Sahani Pathiraja
We consider the problem of sampling from a probability distribution . It is well known that this can be written as an optimisation problem over the space of probability distrib…
An operator splitting analysis of Wasserstein--Fisher--Rao gradient flows
Francesca Romana Crucinio, Sahani Pathiraja
Wasserstein-Fisher-Rao (WFR) gradient flows have been recently proposed as a powerful sampling tool that combines the advantages of pure Wasserstein (W) and pure Fisher-Rao (FR) gr…
Sequential Monte Carlo approximations of Wasserstein--Fisher--Rao gradient flows
Francesca R. Crucinio, Sahani Pathiraja
We consider the problem of sampling from a probability distribution . It is well known that this can be written as an optimisation problem over the space of probability distrib…
An optimal experimental design approach to sensor placement in continuous stochastic filtering
Sahani Pathiraja, Claudia Schillings, Philipp Wacker
Sequential filtering and spatial inverse problems assimilate data points distributed either temporally (in the case of filtering) or spatially (in the case of spatial inverse probl…
PULASki: Learning inter-rater variability using statistical distances to improve probabilistic segmentation
Soumick Chatterjee, Franziska Gaidzik, Alessandro Sciarra +5
In the domain of medical imaging, many supervised learning based methods for segmentation face several challenges such as high variability in annotations from multiple experts, pau…
Connections between sequential Bayesian inference and evolutionary dynamics
Sahani Pathiraja, Philipp Wacker
It has long been posited that there is a connection between the dynamical equations describing evolutionary processes in biology and sequential Bayesian learning methods. This manu…