Statistical signal processing applications usually require the estimation of some parameters
of interest given a set of observed data. These estimates are typically obtained either by …

The adjoint sensitivity method scalably computes gradients of solutions to ordinary
differential equations. We generalize this method to stochastic differential equations …

Now in its second edition, this accessible text presents a unified Bayesian treatment of state-
of-the-art filtering, smoothing, and parameter estimation algorithms for non-linear state …

Hundreds of millions of people lack access to electricity. Decentralized solar-battery systems
are key for addressing this while avoiding carbon emissions and air pollution but are …

The present paper reviews recent activities on inverse analysis strategies in geotechnical
engineering using Kalman filters, nonlinear Kalman filters, and Markov chain Monte Carlo …

Gaussian process-based machine learning is a powerful Bayesian paradigm for
nonparametric nonlinear regression and classification. In this article, we discuss …

We consider parameter estimation in non-linear state space models by using expectation-
maximization based numerical approximations to likelihood maximization. We present a …

The foot-mounted inertial navigation system is an important method of pedestrian navigation
as it, in principle, does not rely any external assistance. A real-time range decomposition …

The past decade has evidenced the increased prevalence of irregularly spaced longitudinal
data in social sciences. Clearly lacking, however, are modeling tools that allow researchers …

A dynamical system is a system of variables that show some regularity in how they evolve
over time. Change concepts described in most dynamical systems models are by no means …