1 citations · 1 across the 6 of their papers we have counts for
8 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…
The Noise Covariances of Linear Gaussian Systems with Unknown Inputs Are Not Uniquely Identifiable Using Autocovariance Least-squares
He Kong, Salah Sukkarieh, Travis J. Arnold +2
Existing works in optimal filtering for linear Gaussian systems with arbitrary unknown inputs assume perfect knowledge of the noise covariances in the filter design. This is imprac…
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…
Accelerated Sparse Bayesian Learning via Screening Test and Its Applications
Yiping Jiang, Tianshi Chen
In high-dimensional settings, sparse structures are critical for efficiency in term of memory and computation complexity. For a linear system, to find the sparsest solution provide…