The basic distribution probability Tutorial for Deep Learning Researchers
distribution-is-all-you-need is the basic distribution probability tutorial for most common distribution focused on Deep learning using python library.
conjugate
means it has relationship of conjugate distributions.
In Bayesian probability theory, if the posterior distributions p(θ | x) are in the same probability distribution family as the prior probability distribution p(θ), the prior and posterior are then called conjugate distributions, and the prior is called a conjugate prior for the likelihood function. Conjugate prior, wikipedia
Multi-Class
means that Random Varivance are more than 2.
N Times
means that we also consider prior probability P(X).
To learn more about probability, I recommend reading [pattern recognition and machine learning, Bishop 2006].
Gamma(a,1) / Gamma(a,1) + Gamma(b,1)
is same with Beta(a,b)
.If you would like to see the details about relationship of distribution probability, please refer to this.