1. https://www.youtube.com/watch?v=Pahyv9i_X2k https://www.youtube.com/watch?v=U1HbB0ATZ_A&list=PLFDbGp5YzjqXQ4oE4w9GVWdiokWB9gEpm https://www.youtube.com/watch?v=U1HbB0ATZ_A&list=PLFDbGp5YzjqXQ4oE4w9GVWdiokWB9gEpm
2. Great Explanation https://www.youtube.com/watch?v=Z6HGJMUakmc https://www.youtube.com/watch?v=-1dYY43DRMA
3. Some explanation https://www.youtube.com/watch?v=0F0QoMCSKJ4&list=RDQMA1bwyF-0P3w&start_radio=1 https://www.youtube.com/watch?v=OTO1DygELpY&list=RDQMA1bwyF-0P3w&index=2 https://www.youtube.com/watch?v=5NMxiOGL39M
4. In Bayesian models - the response variable is assumed to be sampled from a distribution
4.1. So lets say you want to predict a grade of a student - the proper question to ask is what is the probability that the student grade lies between 30-40 (out of hundred), just like we do in distribution. The distribution of the outcome variable is most often assumed to be normal distribution
4.1.1. https://opendatascience.com/how-bayesian-machine-learning-works/
4.1.2. https://towardsdatascience.com/estimating-probabilities-with-bayesian-modeling-in-python-7144be007815
4.2. Parameters are also assumed to be sampled from a distribution
4.2.1. https://towardsdatascience.com/bayesian-linear-regression-in-python-using-machine-learning-to-predict-student-grades-part-1-7d0ad817fca5 https://towardsdatascience.com/bayesian-linear-regression-in-python-using-machine-learning-to-predict-student-grades-part-2-b72059a8ac7e
5. Bayesian ML - based on Bayes T heorem Typically A is weights, B is your response variable - so we are calculating weights given a response variable
5.1. P(A|B): Posterior probability. P(A): Prior probability. Sometimes P(B|A) is referred to as the likelihood and P(B) is referred to as the evidence. P(B|A): Likelihood. P(B): Evidence.
5.1.1. Posterior = Likelihood * Prior / Evidence
5.1.2. Here is a detailed distribution where the formula comes from https://towardsdatascience.com/how-to-use-bayesian-inference-for-predictions-in-python-4de5d0bc84f3 https://analyticsindiamag.com/a-guide-to-bayesian-statistics-in-python-for-beginners/