All articles A–Z

Stoat

Stoat (Mustela erminea), also called ermine or short-tailed weasel: a circumpolar mustelid with a black-tipped tail and white winter coat. Least Concern globally, but a devastating invasive predator of native birds in New Zealand.

Stobart (logistics company)

Stobart, trading as Eddie Stobart Ltd, is a British transport and logistics company headquartered in Warrington, founded in 1970 and owned by Culina Group since 2021.

Oct 21, 2026

Stobi

Stobi (Стоби, Στόβοι) is an ancient city near Gradsko, North Macedonia, at the Vardar-Crna confluence; a Paeonian town, Roman municipium, and capital of Macedonia Secunda, famed for mosaics, theatre, and ongoing Theodosian Palace conservation.

Stochastic

Stochastic, or stochasticity, describes systems and models that involve randomness, so the same starting conditions can produce different results; the central object is the stochastic process.

Stochastic acceleration (second-order Fermi)

Stochastic acceleration, also called second-order Fermi or Fermi-II acceleration, energizes charged particles by repeated scattering off moving magnetized turbulence; Enrico Fermi proposed it in 1949 to explain cosmic rays.

Stochastic actor-oriented model

The stochastic actor-oriented model (SAOM) is a statistical model for longitudinal social network data, treating network change as the accumulated choices of individual actors; it is fitted with the R package RSiena.

Stochastic approximation

Stochastic approximation is a family of iterative algorithms that find the root of an unknown function from noisy observations, introduced by Robbins and Monro in 1951.

Stochastic barrier functions

Stochastic barrier functions are scalar functions of a system's state that yield computable lower bounds on the probability that a noisy dynamical system stays inside a safe set.

Stochastic block model

The stochastic block model, introduced by Holland, Laskey, and Leinhardt in 1983, is a statistical model for networks in which edge probabilities depend only on nodes' blocks.

Stochastic calculus

Stochastic calculus, also known as stochastic analysis or stochastic integration, extends calculus to random processes like Brownian motion, using the Itô integral and Itô's formula.

Stochastic computing

Stochastic computing is a computation paradigm that represents numbers as probabilities of 1s in random bit streams, letting simple logic gates multiply and add, suited to low-power hardware.

Stochastic control

Stochastic control is the branch of control theory and applied probability that finds optimal decision rules for random dynamical systems, minimizing expected cost or maximizing reward.

Stochastic cooling

Stochastic cooling is a beam-cooling technique invented by Simon van der Meer at CERN around 1970 that reduces the energy spread of particle beams in storage rings.

Stochastic density functional theory

Stochastic density functional theory, or sDFT, is a form of Kohn-Sham DFT that estimates the electron density statistically from random orbitals rather than computing every occupied orbital explicitly.

Stochastic differential equation

A stochastic differential equation (SDE) is a differential equation containing a stochastic process, typically a drift term plus noise driven by Brownian motion, used in finance, biology, and physics.

Stochastic dominance

Stochastic dominance is a statistical method for ranking risky prospects by comparing their cumulative distribution functions, without assuming a specific utility function; first-order dominance requires only increasing preferences.

Stochastic filtering

Stochastic filtering is a class of methods in probability and signal processing that estimate the hidden state of a dynamical system from noisy observations, updating the estimate recursively.

Stochastic frontier analysis

Stochastic frontier analysis (SFA) is an econometric method, proposed in 1977, that estimates production or cost frontiers and splits each firm's deviation into inefficiency and random noise.

Stochastic frontier model

The stochastic frontier model is an econometric regression model proposed in 1977 that estimates production or cost frontiers, separating random statistical noise from technical inefficiency.

Stochastic gradient descent

Stochastic gradient descent (SGD), also called incremental gradient descent, is an iterative optimization algorithm that updates model parameters using a gradient estimated from a random subset of training data.

Stochastic gravitational-wave background

The stochastic gravitational-wave background (SGWB, often GWB) is a random gravitational-wave signal from many weak, unresolved sources, with pulsar timing arrays reporting evidence in 2023.

Stochastic gravity

Stochastic gravity is the extension of semiclassical gravity that adds stress-energy fluctuations as metric noise via the Einstein–Langevin equation, developed mainly in the 1990s by Enric Verdaguer and Bei-Lok Hu.

Stochastic GW

Stochastic GW (sGW), introduced by Daniel Neuhauser and colleagues in 2014, is a computational chemistry method using stochastic orbitals to reduce GW's scaling from fourth order to near-linear.

Stochastic heat engine

A stochastic heat engine is a microscopic device that converts heat into work using a few strongly fluctuating degrees of freedom, so its output is random.

Stochastic heat equation

The stochastic heat equation is a stochastic partial differential equation adding random noise to the classical heat equation; it is a prototypical SPDE studied across dimensions.

Stochastic integral

A stochastic integral is an integral with respect to a random process such as Brownian motion, introduced by Kiyosi Itô in 1944 as the basis of stochastic calculus.

Stochastic magnetic tunnel junction

A stochastic magnetic tunnel junction is a magnetic tunnel junction that switches randomly between two resistance states with tunable probability, used as a true random-number generator and probabilistic bit.

Stochastic matrix

A stochastic matrix, also called a Markov matrix or transition probability matrix, is a square matrix whose rows or columns sum to 1, so each is a probability distribution.

Stochastic maximum principle

The stochastic maximum principle states necessary conditions for optimality of controls of systems governed by stochastic differential equations, generalizing Pontryagin's deterministic maximum principle to the stochastic setting.