Stochastic gradient descent (often abbreviated SGD) is an iterative method for optimizing an objective function with suitable smoothness properties (e...

Stochastic hill climbing is a variant of the basic hill climbing method. While basic hill climbing always chooses the steepest uphill move, "stochastic...

Stochastic Neighbor Embedding originally developed by Geoffrey Hinton and Sam Roweis, where Laurens van der Maaten proposed the t-distributed variant...

A stochastic simulation is a simulation of a system that has variables that can change stochastically (randomly) with individual probabilities. Realizations...

algorithm or stochastic simulation algorithm, the SSA) generates a statistically correct trajectory (possible solution) of a stochastic equation system...

own previous values and on a stochastic term (an imperfectly predictable term); thus the model is in the form of a stochastic difference equation (or recurrence...

In statistics, stochastic volatility models are those in which the variance of a stochastic process is itself randomly distributed. They are used in the...

Stochastic computing is a collection of techniques that represent continuous values by streams of random bits. Complex computations can then be computed...

The stochastic block model is a generative model for random graphs. This model tends to produce graphs containing communities, subsets of nodes characterized...

stochastic vector, since the product of any two stochastic matrices is a stochastic matrix, and the product of a stochastic vector and a stochastic matrix...