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Footnotes

1. M. Burkhart, D. Brandman, B. Franco, L. Hochberg, & M. Harrison. The Discriminative Kalman Filter for Bayesian Filtering with Nonlinear and Nongaussian Observation Models. Neural Computation 32 (2020) [link] [implementation] ↩

2. M. Burkhart. “A Discriminative Approach to Bayesian Filtering with Applications to Human Neural Decoding.” Ph.D. Dissertation, Brown University (2019) [link] ↩

3. D. Brandman, M. Burkhart, J. Kelemen, B. Franco, M. Harrison, & L. Hochberg. Robust Closed-Loop Control of a Cursor in a Person with Tetraplegia using Gaussian Process Regression. Neural Computation 30 (2018) [link] ↩

4. D. Brandman, T. Hosman, J. Saab, M. Burkhart, B. Shanahan, J. Ciancibello, et al. Rapid calibration of an intracortical brain computer interface for people with tetraplegia. Journal of Neural Engineering 15 (2018) [link] ↩

5. M. Burkhart. Discriminative Bayesian filtering lends momentum to the stochastic Newton method for minimizing log-convex functions. Optimization Letters 17 (2023) [link] ↩