Further reading
To learn more about the topics that were covered in this chapter, take a look at the following resources.
- The Gradient Boosters VI(B): NGBoost: https://deep-and-shallow.com/2020/06/27/the-gradient-boosters-vib-ngboost/
- Dive into Deep Learning, Chapter 5.6: https://d2l.ai/chapter_multilayer-perceptrons/dropout.html
- Bayesian Inference by Marco Taboga: https://www.statlect.com/fundamentals-of-statistics/Bayesian-inference
- Seeing Theory: Bayesian Inference: https://seeing-theory.brown.edu/bayesian-inference/index.html
- A Tutorial on Sparse Gaussian Processes and Variational Inference by Felix Leibfried et al.: https://arxiv.org/pdf/2012.13962
- A Gentle Introduction to Conformal Prediction and Distribution-Free Uncertainty Quantification by Anastasios N. Angelopoulos and Stephen Bates. (2021): https://arxiv.org/abs/2107.07511
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