Notation is a crucial aspect of probability theory. It provides a standardized language to express key concepts and events. The sample space (Ω) represents the set of all possible outcomes. An event (E) is a subset of the sample space. Probability (P(E)) measures the likelihood of an event occurring. Random variables (X) are functions that assign numerical values to each outcome in the sample space. The probability mass function (p(x)) assigns probabilities to individual values of a discrete random variable, while the probability density function (f(x)) does the same for continuous random variables. These notations are essential for understanding the fundamentals of probability theory and interpreting statistical results.
