- Prior Probability : a prior probability distribution, often simply called the prior, of an uncertain quantity is the probability distribution that would express one’s beliefs about this quantity before some evidence is taken into account (~Ex.: )
- Posterior Probability: results from updating the prior probability with information summarized by the likelihood, through an application of Bayes’ theorem.
- Likelihood: The likelihood function (often simply called the likelihood) is the joint probability of the observed data viewed as a function of the parameters, the likelihood may be found written as to emphasize that it provides the probability of observing sample given the vector of the parameters.
~Ex.: Some Feature Extractions
==First 2 Principal Components== : the first 2 eigen-vectors of the covariance matrix of the Gaussian distribution of the data.
