A Discriminative Approach to Bayesian Filtering with Applications to Human Neural Decoding

17 Jul 2018Michael C. Burkhart

Given a stationary state-space model that relates a sequence of hidden states and corresponding measurements or observations, Bayesian filtering provides a principled statistical framework for inferring the posterior distribution of the current state given all measurements up to the present time. For example, the Apollo lunar module implemented a Kalman filter to infer its location from a sequence of earth-based radar measurements and land safely on the moon... (read more)

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