Estimation of functions from sparse and noisy data is a central theme in machine learning. In
the last few years, many algorithms have been developed that exploit Tikhonov …

Most of the currently used techniques for linear system identification are based on classical
estimation paradigms coming from mathematical statistics. In particular, maximum likelihood …

Master Techniques and Successfully Build Models Using a Single Resource Vital to all data-
driven or measurement-based process operations, system identification is an interface that …

Deep learning is a topic of considerable current interest. The availability of massive data
collections and powerful software resources has led to an impressive amount of results in …

System identification learns mathematical models of dynamic systems starting from input–
output data. Despite its long history, such research area is still extremely active. New …

One of the key challenges in identifying nonlinear and possibly non-Gaussian state space
models (SSMs) is the intractability of estimating the system state. Sequential Monte Carlo …

Bayesian nonlinear system identification for one of the major classes of dynamic model, the
nonlinear autoregressive with exogenous input (NARX) model, has not been widely studied …

All the approaches for hybrid system identification appeared in the literature assume that
model complexity is known. Popular models are eg piecewise ARX with a priori fixed orders …

This paper is concerned with parameter estimation in linear and non-linear Itô type
stochastic differential equations using Markov chain Monte Carlo (MCMC) methods. The …

Reproducing real world dynamics in simulation is critical for the development of new control
and perception methods. This task typically involves the estimation of simu-lation parameter …