1 citations · 1 across the 4 of their papers we have counts for
5 papers
On Embeddings and Inverse Embeddings of Input Design for Regularized System Identification
Biqiang Mu, Tianshi Chen, He Kong +3
Input design is an important problem for system identification and has been well studied for the classical system identification, i.e., the maximum likelihood/prediction error meth…
An Efficient Implementation for Spatial-Temporal Gaussian Process Regression and Its Applications
Junpeng Zhang, Yue Ju, Biqiang Mu +2
Spatial-temporal Gaussian process regression is a popular method for spatial-temporal data modeling. Its state-of-art implementation is based on the state-space model realization o…
Identifiability Analysis of Noise Covariances for LTI Stochastic Systems with Unknown Inputs
He Kong, Salah Sukkarieh, Travis J. Arnold +3
Most existing works on optimal filtering of linear time-invariant (LTI) stochastic systems with arbitrary unknown inputs assume perfect knowledge of the covariances of the noises i…
On the Asymptotic Optimality of Cross-Validation based Hyper-parameter Estimators for Regularized Least Squares Regression Problems
Biqiang Mu, Tianshi Chen, Lennart Ljung
The asymptotic optimality (a.o.) of various hyper-parameter estimators with different optimality criteria has been studied in the literature for regularized least squares regressio…
Supplementary Material for CDC Submission No. 1461
Yue Ju, Tianshi Chen, Biqiang Mu +1
In this paper, we focus on the influences of the condition number of the regression matrix upon the comparison between two hyper-parameter estimation methods: the empirical Bayes (…