Bernoulli Distribution
The mean of a bernoulli distribution, also called the expected value, represents the long run average outcome of repeated independent trials.
Read MoreThe mean of a bernoulli distribution, also called the expected value, represents the long run average outcome of repeated independent trials.
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Binomial distribution is a probability distribution, widely used to model the number of successes in a fixed number of independent random trials, where each trial has only two possible outcomes: success or failure. This distribution is useful for calculating the probability of a specific number of successes in scenarios like flipping coins, quality control, or survey predictions.

A geometric distribution is a discrete probability distribution that gives the probability that the first success occurs on a specific trial in a sequence of independent Bernoulli trials, where each trial has two outcomes, success or failure, and the probability of success, p, remains constant across trials.

The negative binomial distribution models the number of trials needed to achieve a specified number of successes in a sequence of independent and identically distributed Bernoulli trials.

A complete, visual guide to the Normal (Gaussian) distribution: the bell curve formula, notation, mean and standard deviation, symmetry, skewness and kurtosis, the empirical rule, the standard normal table, worked examples, related distributions, and where it shows up in statistics and machine learning.

The Poisson distribution is a discrete probability distribution that calculates the likelihood of a certain number of events occurring within a fixed interval of time, assuming the events occur independently.

A complete, visual guide to the Uniform distribution: the discrete and continuous forms, the flat-density formula, notation, mean and variance, the CDF, moments and the moment generating function, worked examples, related distributions, and where it shows up in statistics, simulation and machine learning.