Imaging is center stage in biology. Advances in microscopy and labeling techniques have
enabled unprecedented observations and continue to inspire new developments. Efficient …

Particle Filters (PFs) are Sequential Monte Carlo methods which are widely used to solve
filtering problems of dynamic models under Non-Linear Non-Gaussian noise. Modern PF …

Resampling is a well-known statistical algorithm that is commonly applied in the context of
Particle Filters (PFs) in order to perform state estimation for non-linear non-Gaussian …

Abstract Sequential Monte Carlo (SMC) methods are a well-established family of Bayesian
inference algorithms for performing state estimation for Non-Linear Non-Gaussian models …

We investigate a class of DNA mixture deconvolution algorithms based on variational
inference, and we show that this can significantly reduce computational runtimes with little or …

Particle filters are key algorithms for object tracking under non-linear, non-Gaussian
dynamics. The high computational cost of particle filters, however, hampers their …

The distributed resampling algorithm with non-proportional allocation (RNA)[1] is key to
implementing particle filtering applications on parallel computer systems. We extend the …

Tracking fluorescent particles in microscopy image sequences is pivotal in obtaining
quantitative characterizations of the dynamical processes underlying these fluorescent …

Particle filters are powerful methods for state estimation in nonlinear/non-Gaussian
dynamical systems. However due to the heavy computational requirements, they may not …

Particle filters (PF) are key algorithms for object tracking under non-linear, non-Gaussian
dynamics. The high computational cost of particle filters, however, hampers their …