Nonlinear non-Gaussian state-space models are ubiquitous in statistics, econometrics,
information engineering and signal processing. Particle methods, also known as Sequential …

Hidden Markov models—most often abbreviated to the acronym “HMMs”—are one of the
most successful statistical modelling ideas that have came up in the last forty years: the use …

Particle methods are popular computational tools for Bayesian inference in nonlinear non-
Gaussian state space models. For this class of models, we present two particle algorithms to …

Online (also called “recursive” or “adaptive”) estimation of fixed model parameters in hidden
Markov models is a topic of much interest in times series modeling. In this work, we propose …

As one of most important aspects of condition-based maintenance (CBM), failure prognosis
has attracted an increasing attention with the growing demand for higher operational …

Wind power production data at temporal resolutions of a few minutes exhibit successive
periods with fluctuations of various dynamic nature and magnitude, which cannot be …

The performance of Hidden Markov Models (HMMs) targeted for complex real-world
applications are often degraded because they are designed a priori using limited training …

To ensure safety of satellite operation in orbit, it is important to estimate the capacity of
lithium-ion battery in time. However, the battery capacity cannot be measured directly in …

We address the problem of approximating the posterior probability distribution of the fixed
parameters of a state-space dynamical system using a sequential Monte Carlo method. The …

We present a variational method for online state estimation and parameter learning in state-
space models (SSMs), a ubiquitous class of latent variable models for sequential data. As …