[Re] The Discriminative Kalman Filter for Bayesian Filtering with
  Nonlinear and Non-Gaussian Observation Models
Josue Casco-Rodriguez Caleb Kemere Richard G. Baraniuk
Kalman filters provide a straightforward and interpretable means to estimate
hidden or latent variables, and have found numerous applications in control,
robotics, signal processing, and machine learning. One such application is
neural decoding for neuroprostheses. In 2020, Burkhart et al. thoroughly
evaluated their new version of the Kalman filter that leverages Bayes' theorem
to improve filter performance for highly non-linear or non-Gaussian observation
models. This work provides an open-source Python alternative to the authors'
MATLAB algorithm. Specifically, we reproduce their most salient results for
neuroscientific contexts and further examine the efficacy of their filter using
multiple random seeds and previously unused trials from the authors' dataset.
All experiments were performed offline on a single computer.