Abstract: In nonlinear system identification, Volterra kernel estimation based on regularized least squares can be performed by taking a Bayesian approach. In this framework, covariance structures ...
Accurate channel-estimation algorithms are critical for enhancing the throughput of wireless communication systems, including millimetre wave (mmWave) multiple-input multiple-output (MIMO) systems, ...
This project implements state-of-the-art deep learning models for financial time series forecasting with a focus on uncertainty quantification. The system provides not just point predictions, but ...
This repository contains code to generate a R package ("contamMix") that implements a mixture-model used to estimate contamination in ancient mitochondrial DNA using MCMC. The model is detailed in ...
Abstract: Hyperparameter estimation is a critical aspect of kernel-based regularization methods (KRMs), alongside kernel design. Empirical Bayes (EB) and Stein's unbiased risk estimator (SURE) are two ...
When handling real-world data modeled by a complex network dynamical system, the number of the parameters is often much more than the size of the data. Therefore, in many cases, it is impossible to ...
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