Nonlinear state-space models are ubiquitous in modeling real-world dynamical systems.
Sequential Monte Carlo (SMC) techniques, also known as particle methods, are a well …

Due to the complex and non-linear characteristics of landslide evolution, the deformation
law of landslides is always hard to be predicted. Considering the dynamical evolution …

State-space models (SSMs) are a rich class of dynamical models with a wide range of
applications in economics, healthcare, computational biology, robotics, and more. Proper …

We provide a proof-of-concept for a novel state-space modelling approach for predicting
monthly deaths due to political violence. Attention is focused on developing the method and …

Gaussian process regression is a popular method for non-parametric probabilistic modeling
of functions. The Gaussian process prior is characterized by so-called hyperparameters …

It has recently been shown that many of the existing quasi-Newton algorithms can be
formulated as learning algorithms, capable of learning local models of the cost functions …

Maximum likelihood (ML) estimation using Newton's method in nonlinear state space
models (SSMs) is a challenging problem due to the analytical intractability of the …

The ability to monitor the quality of the metal forming process as well as the machine's
condition is of significant importance in modern industrial processes. In the case where a …

Gaussian process (GP) regression is widely used to find the extreme of a black-box function
by iteratively approximating an objective function when new evaluation obtained. Such …

Demand for learning, design and decision making is higher than ever before. Autonomous
vehicles need to learn how to ride safely by recognizing pedestrians, traffic signs, and other …