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Discrete Bayes Filter This notebook introduces Bayesian filtering through a practical example of tracking a dog in a hallway. It demonstrates how to combine noisy sensor measurements with movement predictions to estimate position.

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Key Concepts

  • Prior and Posterior Distributions: How beliefs are updated with new information
  • Prediction Step: Incorporating movement uncertainty through convolution
  • Update Step: Using Bayes’ theorem to incorporate measurements
  • Multimodal Distributions: Handling multiple hypotheses about position
This notebook is adapted from the excellent book Kalman and Bayesian Filters in Python by Roger Labbe.

Run the Tutorial

Open the original interactive notebook in Google Colab to execute the code and explore the concepts.

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Notebook Preview

Key references: (Lukežič et al., 2016; Karl et al., 2016; Frazier, 2018; Wilson et al., 2011)

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References

  • Frazier, P. (2018). A Tutorial on Bayesian Optimization.
  • Karl, M., Soelch, M., Bayer, J., Smagt, P. (2016). Deep Variational Bayes Filters: Unsupervised Learning of State Space Models from Raw Data.
  • Lukežič, A., Vojíř, T., Čehovin, L., Matas, J., Kristan, M. (2016). Discriminative correlation filter with channel and Spatial Reliability.
  • Wilson, A., Knowles, D., Ghahramani, Z. (2011). Gaussian Process Regression Networks.
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