State‐space models are an increasingly common and important tool in the quantitative
ecologists' armoury, particularly for the analysis of time‐series data. This is due to both their …

By approximating posterior distributions with weighted samples, particle filters (PFs) provide
an efficient mechanism for solving non-linear sequential state estimation problems. While …

We investigate the low-dimensional structure of deterministic transformations between
random variables, ie, transport maps between probability measures. In the context of …

In this paper, a novel outlier-resistant filtering problem is concerned for a class of networked
systems with dead-zone-like censoring under the weighted try-once-discard protocol …

Unscented Kalman filter (UKF) has been extensively used for state estimation of nonlinear
stochastic systems, which suffers from performance degradation and even divergence when …

Partially-observed Boolean dynamical systems (POBDS) are a general class of nonlinear
state-space models that provide a rich framework for modeling many complex dynamical …

Particle filters are broadly used to approximate posterior distributions of hidden states in
state-space models by means of sets of weighted particles. While the convergence of the …

In this work, we combine a stochastic model reduction with a particle filter augmented with
tempering and jittering, and apply the combined algorithm to a damped and forced …

Time-series datasets are central in machine learning with applications in numerous fields of
science and engineering, such as biomedicine, Earth observation, and network analysis …

We propose nested sequential Monte Carlo (NSMC), a methodology to sample from
sequences of probability distributions, even where the random variables are high …