A Bayesian framework that models the impulse response as a stochastic process with mean and fluctuation terms recovers linear time-invariant systems from single noisy pairs and tracks smoothly varying systems using Gaussian process priors.
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Bayesian Modeling and Estimation of Linear Time-Varying Systems using Neural Networks and Gaussian Processes
A Bayesian framework that models the impulse response as a stochastic process with mean and fluctuation terms recovers linear time-invariant systems from single noisy pairs and tracks smoothly varying systems using Gaussian process priors.