AI Networking Cookbook: Practical recipes for AI-assisted network automation and development
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We consider estimating the one-step-ahead conditional distribution of a multivariate stochastic process. Many existing approaches rely on assumptions such as finite-range memory, sparsity, or additivity, which can be poorly suited to processes with long-range nonlinear interactions. However, without such structural ass...
It is shown that, for a large class of dynamic models, parameters can be estimated by matching a small number of random features of the observed and simulated data and serves as the foundation for a new class of random feature methods for simulation-based estimation and inference.
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