Markovian Gaussian processes: A lot of theory and some practical stuff
Read OriginalThis technical blog post delves into Markovian Gaussian Processes, discussing the computational challenges of standard GPs and how leveraging the Markov property and sparse matrix computations can improve efficiency. It covers theoretical foundations, including reproducing Kernel Hilbert spaces (RKHS), and contrasts covariance function approaches with RKHS-based modeling for greater flexibility.
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