This book presents a wealth of machine learning techniques to make the control of robots more flexible and safe when interacting with humans.

This book aims to provide an overview of some adaptive techniques used in estimating parameters for finite populations where the sampling at any stage depends on the sampling information obtained to date.

Written by two acknowledged experts in this emerging field, this book offers researchers their first comprehensive introduction to adaptive sampling.

Unifying the most important methodology in this field, Multi-Resolution Methods for Modeling and Control of Dynamical Systems explores existing approximation methods as well as develops new ones for the approximate solution of large-scale ...

This book describes in detail sampling techniques that can be used for unsupervised and supervised cases, with a focus on sampling techniques for machine learning algorithms.

Reliability/safety analysis of stochastic dynamic systems (e.g., nuclear power plants, airplanes, chemical plants) is currently performed through a combination of Event-Tress and Fault-Trees.

In this thesis, we focus on Bayesian learning for a particular class of models: high-dimensional nonlinear dynamical systems, which have been commonly used to predict a wide range of transient phenomena including fluid flows, heat transfer, ...