Bayesian Statistics and Machine Learning
"Bayesian inference is a method of statistical inference in which Bayes' theorem is used to update the probability for a hypothesis as more evidence or information becomes available. Bayesian inference is an important technique in statistics, and especially in mathematical statistics."
en.wikipedia.org › wiki › Bayesian_inference
Bayesian inference - Wikipedia
"Firstly, (statistical) inference is the process of deducing properties about a population or probability distribution from data"
"Bayesian inference is therefore just the process of deducing properties about a population or probability distribution from data using Bayes’ theorem. That’s it."
https://towardsdatascience.com/probability-concepts-explained-bayesian-inference-for-parameter-estimation-90e8930e5348
Introduction to Bayesian Inference
https://blogs.oracle.com/datascience/introduction-to-bayesian-inference
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Bayesian Linear Regression
In the Bayesian viewpoint, we formulate linear regression using probability distributions rather than point estimates. The response, y, is not estimated as a single value, but is assumed to be drawn from a probability distribution. The model for Bayesian Linear Regression with the response sampled from a normal distribution is:"
https://towardsdatascience.com/introduction-to-bayesian-linear-regression-e66e60791ea7
"Bayesian model selection
Tom Minka
Bayesian model selection uses the rules of probability theory to select among different hypotheses. It is completely analogous to Bayesian classification."
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http://alumni.media.mit.edu/~tpminka/statlearn/demo/
Logistic Regression from Bayes' Theorem
https://www.countbayesie.com/blog/2019/6/12/logistic-regression-from-bayes-theorem
Kernel Trick and Kernels
https://svivek.com/teaching/lectures/slides/svm/kernels.pdf
"When talking about kernels in machine learning, most likely the first thing that comes into your mind is the support vector machines (SVM)"
https://medium.com/@zxr.nju/what-is-the-kernel-trick-why-is-it-important-98a98db0961d
Gaussian Processes
"or why I don’t use SVMs"
https://mlss2011.comp.nus.edu.sg/uploads/Site/lect1gp.pdf
Gaussian Process Classification and Active Learning with Multiple Annotators
http://proceedings.mlr.press/v32/rodrigues14.pdf
"Assumed density filtering is an online inference algorithm. that incrementally updates the posterior over W after ob- serving new evidence."
https://www.aaai.org/ocs/index.php/AAAI/AAAI16/paper/download/12391/11777
"Expectation propagation (EP) is a technique in Bayesian machine learning. EP finds approximations to a probability distribution. It uses an iterative approach that leverages the factorization structure of the target distribution.
en.wikipedia.org › wiki › Expectation_propagation
Expectation propagation - Wikipedia
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rejection sampling
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In numerical analysis and computational statistics, rejection sampling is a basic technique used to generate observations from a distribution. It is also commonly called the acceptance-rejection method or "accept-reject algorithm" and is a type of exact simulation method. The method works for any distribution in 
https://en.wikipedia.org/wiki/Rejection_sampling
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Bayesian Deep learning
"However, graphical networks such as Bayesian networks are useful to model uncertainty along with causal inference and logic deduction. In this regard, Bayesian Deep learning combines perception (deep learning) with strong probabilistic inference which can estimate uncertainty.
www.quora.com › How-different-is-Bayesian-deep-learning-from-deep-...
How different is Bayesian deep learning from deep learning? - Quora
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Note: Older short-notes from this site are posted on Medium: https://medium.com/@SayedAhmedCanada
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Sayed Ahmed
BSc. Eng. in Comp. Sc. & Eng. (BUET)
MSc. in Comp. Sc. (U of Manitoba, Canada)
MSc. in Data Science and Analytics (Ryerson University, Canada)
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