Probabilistic Models

Abstract

In this chapter we will study a new type of algorithm based on probability and statistics. We will study the two fundamental approaches in the form of discriminative and generative models. In the discriminative models we will study the concepts of Bayesian approach and Maximum likelihood approach. We will derive the solution of a same problem using both approaches to illustrate the differences and advantages and disadvantages. Then we will study the probability density functions and cumulative density functions of some commonly used distributions. Although it might feel repetitive and unimportant to study these wide variety of distributions one after another, I would highly recommend to go through them nonetheless. These distributions and their specific properties are quite important in understanding the variety of ways in which one can expect the data to be distributed in. This knowledge can be invaluable in data exploration.