State-space digital filters constitute a versatile class of algorithms used to process signals by modelling dynamic systems through state variables. Their representation encompasses both the system’s ...

Particle methods are popular computational tools for Bayesian inference in nonlinear non-Gaussian state space models. For this class of models, we present two particle algorithms to compute the score ...

This paper derives an expression for the likelihood for a state space model. The expression can be evaluated with the Kalman filter initialized at a starting state ...

For a while now, we’ve been talking about transformers, frontier neural network logic models, as a transformative technology, no pun intended. But now, these attention mechanisms have other competing ...