The Beta Distribution is a continuous probability distribution defined on the interval [0, 1]. It is characterized by two shape parameters, often denoted as α (alpha) and β (beta), which determine the form of the distribution. This makes it versatile for modeling random variables that represent proportions or probabilities, such as the likelihood of success in a series of trials. Commonly used in Bayesian statistics, the Beta Distribution can represent prior beliefs about probabilities before observing data. Its flexibility allows it to take various shapes, including uniform, U-shaped, or J-shaped, depending on the values of α and β. This adaptability makes it useful in fields like finance, biology, and machine learning.