[Re] The Discriminative Kalman Filter for Bayesian Filtering with Nonlinear and Non-Gaussian Observation Models

• URL: http://arxiv.org/abs/2401.14429v1
• Date: Wed, 24 Jan 2024 21:00:42 GMT
• Title: [Re] The Discriminative Kalman Filter for Bayesian Filtering with Nonlinear and Non-Gaussian Observation Models
• Authors: Josue Casco-Rodriguez, Caleb Kemere, Richard G. Baraniuk
• Abstract summary: Kalman filters provide a straightforward and interpretable means to estimate hidden or latent variables. One such application is neural decoding for neuroprostheses. This work provides an open-source Python alternative to the authors' algorithm for highly non-linear or non-Gaussian observation models.
• Score: 25.587281577467405
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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