The choice of Bernoulli Naive Bayes formulation is important because it leads itself to word-based information planning. By associating each word in the dictionary with a binary random variable, we are able to compute the influence of individual words on class label distribution.

…al space where certain sets of parameter values better explain observed data. Therefore, I think of MCMC methods as randomly sampling inside a probabilistic space to approximate the posterior distribution.

…o Markov chains, which seem like an unreasonable way to model a random variable over a few periods, can be used to compute the long-run tendency of that variable if we understand the probabilities that govern its behavior.