This chapter provides most of the probability concepts necessary to implement the Naive Bayes algorithm and shows how to implement these concepts in R. It works with a dataset that shows the voting records of US senators on 16 issues. The chapter focuses on basic concepts of probability. It discusses the issues of missing values and conditional probability. In a theory of statistics called Bayesian statistics, the following terms are used frequently: prior probability, posterior probability, and likelihood. Two events are called independent events if the fact of one of the events having occurred does not affect the probability of the other event. An example of independent events occurs when we consider an experiment of first tossing a coin and then rolling a die.