The beta distribution is a versatile probability distribution that represents outcomes for percentages or proportions, defined by two shape parameters. Depending on these parameters, the distribution can exhibit various shapes, including uniform, U-shaped, symmetric, or skewed forms. It is notably used in Bayesian statistics as the prior distribution of a binomial probability.

1. Reinforcement Learning

2. Algorithms

24. Thompson Sampling Algorithm

25. Beta
Distribution
A Beta distribution is a type of probability
distribution. This distribution represents a family
of probabilities and is a versatile way to
represent outcomes for percentages or
proportions.
The beta distribution is a continuous distribution
defined by two shape parameters. The
distribution can take on different shapes
depending on the values of the two parameters.
The beta distribution is also used in Bayesian
statistics, for example, as the prior distribution of
a binomial probability.

29. Both Shapes equal 1
The beta distribution is the uniform
distribution.
Both Shapes less than 1
The beta distribution is U-shaped
Both Shapes same & >1
The distribution is symmetric.
First Shape is Greater than Second Shape
The beta distribution is skewed to the left.
First Shape is Less than Second Shape
The beta distribution is skewed to the right.