Time series analysis aims to understand underlying patterns and relationships in data to
inform decision-making. As time series data are becoming more widely available across a …

We present a continuous-time probabilistic approach for estimating the chirp signal and its
instantaneous frequency function when the true forms of these functions are not accessible …

Gaussian Processes (GPs) and Linear Dynamical Systems (LDSs) are essential time series
and dynamic system modeling tools. GPs can handle complex, nonlinear dynamics but are …

Stochastic filtering refers to estimating the probability distribution of the latent stochastic
process conditioned on the observed measurements in time. In this paper, we introduce a …

The aim of this thesis is to develop Hilbert space methods for approximation of integrals
appearing in filtering and smoothing of nonlinear state-space models. State-space models …

Abstract Gaussian Processes (GPs) are a powerful Bayesian approach to regression and
classification problems in machine learning. A GP is a collection of random variables, in …