Bayesian theory

In classification, Bayes' rule is used to calculate the probabilities of the classes. The main aim is related about how we can make rational decisions to minimize expected risk. Bayes' theorem provides a way to calculate the probability of a hypothesis based on its prior probability, the probabilities of observing various data given the hypothesis, and the observed data itself. Probability and inference Data comes from a process that is not completely known. This lack of knowledge is indicating by modelling the process as a random process. In probability and statistics, a Bernoulli process is a discrete-time stochastic process consisting of a sequence of independent random variables taking values over two symbols. Prosaically, a Bernoulli process is coin flipping, possibly with an unfair coin. A variable in such a sequence may be called a Bernoulli variable. (wikipedia) There is a example, normally speaking if you know you got a fair coin the probability will be .5, but, if you suspect that you got a charge coin you can estimate the probability doing a Bernoulli process. Classification, probabilistic model Input/Output Example related about credit scoring and how to establish 2 classes, basically, High and Low risk costumer. A prediction in a form presented, is homologue to the second one (coin and credit decision). The focus is basically analyzing past transactions, the bank is planning to identify good and bad customers from their bank accounts. They have the customer yearly income and savings which are fundamental to build the classification model. Bayes' rule Explain de posterior, prior (historical data), likelihood (conditional probability, IF-THEN rule), and evidence concepts. Join probability, it has to be exhaustive and excluded.