Usually databases are treated primarily as fairly dumb data storage systems, but they can be capable of much more. Case in ...

Kalman filtering has emerged as a pivotal tool in the field of multibody system dynamics, offering a robust framework for real-time state and parameter estimation in systems composed of interconnected ...

Kalman filtering has long served as a foundational tool for state estimation in dynamic systems, offering a robust and efficient means of filtering noise from measured signals. In the realm of ...

Numerical basics -- Method of least squares -- Recursive least-quares filtering -- Polynomial Kalman filters -- Kalman filters in a nonpolynomial world -- Continuous polynomial Kalman filter -- ...

If you program using values that represent anything in the real world, you have probably at least heard of the Kalman filter. The filter allows you to take multiple value estimates and process them ...

This example estimates the normal SSM of the mink-muskrat data using the EM algorithm. The mink-muskrat series are detrended. Refer to Harvey (1989) for details of this data set. Since this EM ...

This is an expository article that develops the Kalman filter from a Cholesky factorization perspective. In particular, the Kalman filter is shown to be a modification of the Cholesky factorization ...

In practical situations, we observe the number of claims to an insurance portfolio but not the claim intensity. It is therefore of interest to try to solve the 'filtering problem'; that is, to obtain ...

This paper proposes a new econometric model for the estimation of optimal hedge ratios (HRs): the Kalman filter error-correction model (KF–ECM). This paper proposes a new econometric model for the ...

In this course, you will learn how to implement different state-of-charge estimation methods and to evaluate their relative merits. Prior knowledge needed: ECEA 5730, ECEA 5731, a Bachelor’s degree in ...