Sequential Monte Carlo methods—also known as particle filters—offer approximate
solutions to filtering problems for nonlinear state-space systems. These filtering problems …

In recent years, MEMS inertial sensors (3D accelerometers and 3D gyroscopes) have
become widely available due to their small size and low cost. Inertial sensor measurements …

Particle Filtering (PF) methods are an established class of procedures for performing
inference in non-linear state-space models. Resampling is a key ingredient of PF necessary …

Particle Markov chain Monte Carlo (PMCMC) is a systematic way of combining the two main
tools used for Monte Carlo statistical inference: sequential Monte Carlo (SMC) and Markov …

The rapid growth of electric vehicles (EV) in cities has led to the development of microgrids
(MGs) combined with photovoltaics (PV) and the energy storage system (ESS) as charging …

When learning the value of actions in volatile environments, humans often make seemingly
irrational decisions that fail to maximize expected value. We reasoned that these 'non …

State-space models have been successfully used for more than fifty years in different areas
of science and engineering. We present a procedure for efficient variational Bayesian …

In this paper, we consider the identification problem for nonlinear state-space models with
skewed measurement noises. The generalized hyperbolic skew Student'st (GHSkewt) …

In this paper, we present a practical algorithm for calibrating a magnetometer for the
presence of magnetic disturbances and for magnetometer sensor errors. To allow for …

A core problem in statistics and probabilistic machine learning is to compute probability
distributions and expectations. This is the fundamental problem of Bayesian statistics and …