A function that gives the likelihood of different outcomes in a random process.A probability distribution is a mathematical function that assigns probabilities to each possible outcome of a random process. It describes how likely different results are, from the roll of a die to the position of an electron in an atom. In physics, probability distributions are essential for understanding systems with inherent randomness, such as quantum particles or thermal fluctuations.
The concept was developed in the 17th century by mathematicians like Blaise Pascal and Pierre de Fermat, and later formalized by figures like Jacob Bernoulli and Carl Friedrich Gauss, whose normal distribution became ubiquitous in physics.
The distribution is defined over a sample space of outcomes. For a discrete variable, it gives a probability for each outcome; for a continuous variable, it gives a probability density. The total probability always sums or integrates to 1. In quantum mechanics, the probability distribution of a particle's position is given by the square of the wavefunction's amplitude, linking the abstract mathematics to observable reality.
Probability distributions are used in statistical mechanics to predict the behavior of gases, in quantum physics to calculate measurement outcomes, and in data analysis to model noise and uncertainty.
The normal distribution, also called the bell curve, appears so often in nature—from heights of people to measurement errors—that it's known as the 'Gaussian' after Carl Friedrich Gauss, who used it to predict the orbit of the dwarf planet Ceres.
| P | variable | Probability distribution over x — probability distribution |
| Q | variable | Probability distribution over x — probability distribution |
| x | variable | Outcome index |
| ∑ | operator | Summation over the discrete sample space — summation |
| |·| | operator | Takes the non-negative magnitude — absolute value |








