This repository contains implementations of various probability distributions in Python. Each distribution is implemented as a separate module, allowing for easy usage and experimentation.
- Bernoulli Distribution: Models a binary outcome with a single parameter representing the probability of success.
- Binomial Distribution: Models the number of successes in a fixed number of independent Bernoulli trials.
- Poisson Distribution: Models the number of events occurring in a fixed interval of time or space.
- Hypergeometric Distribution: Models the number of successes in a fixed number of draws without replacement from a finite population.
- Negative Binomial Distribution: Models the number of failures that occur before a specified number of successes in a sequence of independent Bernoulli trials.
- Geometric Distribution: Models the number of trials needed to achieve the first success in a sequence of independent Bernoulli trials.
- Normal (Gaussian) Distribution: Models continuous random variables with a bell-shaped distribution.
- Uniform Distribution: Models continuous random variables with a constant probability density.
- Exponential Distribution: Models the time between events in a Poisson process.
- Gamma Distribution: Models continuous random variables with a skewed distribution.
- Beta Distribution: Models continuous random variables with values between 0 and 1.
- Log-Normal Distribution: Models continuous random variables whose logarithm is normally distributed.
- Pareto Distribution: Models continuous random variables with a heavy-tailed distribution.
- Weibull Distribution: Models continuous random variables with a flexible shape.