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

The particle filter was popularized in the early 1990s and has been used for solving
estimation problems ever since. The standard algorithm can be understood and …

Two decades ago, with the publication, we witnessed the rebirth of particle filtering (PF) as a
methodology for sequential signal processing. Since then, PF has become very popular …

Temperature and cell aging are two major factors that influence the reliability and safety of Li-
ion batteries. A general battery model considering both temperature and degradation is …

Accurate state-of-charge (SOC) estimation is critical for the safety and reliability of battery
management systems in electric vehicles. Because SOC cannot be directly measured and …

We develop a theory of low-frequency movements in inflation expectations, and use it to
interpret joint dynamics of inflation and inflation expectations for the United States and other …

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 …

Particle filters (PFs) have become popular for assimilation of a wide range of hydrologic
variables in recent years. With this increased use, it has become necessary to increase the …

Origin-destination (OD) pattern estimation is a vital step for traffic simulation applications and
active urban traffic management. Many methods have been proposed to estimate OD …

As an important part of prognostics and health management, accurate remaining useful life
(RUL) prediction for lithium (Li)-ion batteries can provide helpful reference for when to …