Mathematical modelling is nowadays a pivotal tool for infectious diseases studies,
completing regular biological investigations. The rapid growth of computer technology …

Approximate Bayesian computation (ABC) has become one of the major tools of likelihood-
free statistical inference in complex mathematical models. Simultaneously, stochastic …

We consider the modeling of data generated by a latent continuous-time Markov jump
process with a state space of finite but unknown dimensions. Typically in such models, the …

Multi‐state models are frequently applied to represent processes evolving through a discrete
set of states. Important classes of multi‐state models arise when transitions between states …

Abstract The stochastic FitzHugh-Nagumo (FHN) model is a two-dimensional nonlinear
stochastic differential equation with additive degenerate noise, whose first component, the …

There are few computational and methodological tools available for the analysis of general
multi-state models with interval censoring. Here, we propose a general framework for …

Approximate Bayesian computation (ABC) is commonly used for parameter estimation and
model comparison for intractable simulator-based statistical models whose likelihood …

Competing risks and multi-state models allow us to study complex disease settings and
answer composite research questions and should be used more widely in epidemiology …

Stochastic compartmental models provide interpretable probabilistic descriptions of many
dynamic biological phenomena, such as the spread of a contagious disease through a …

Robots often need to communicate their navigation goals to humans to assist observers in
anticipating the robot's future actions. Enabling observers to infer where the robot is going …