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Probabilistic is probably the wider concept. Generally, the terms stochastic and probabilistic are used interchangeably.

Random refer to having unpredictable outcomes and, in the ideal case, all outcomes are equally probable which means there is no dependence on the other observation, for example, tossing a fair coin whereas stochastic is used when we focus on the probabilistic nature of the variable which is randomly determined. Generally, stochastic is used as a synonym for random. Stochastic is used as a synonym for random and probabilistic whereas non-deterministic is different from stochastic. Let’s get a better understanding of stochastic by comparing it with other related terms which are sometimes used as a synonym for stochastic. Random, Probabilistic, and Non-deterministic Now let’s understand the terms- “random,” “probabilistic,” and “non-deterministic” which are also used as a notion of uncertainty, and how they differ from the term- “stochastic”. Although, it is different from “deterministic” but is closely related to “randomness” and “probabilistic.” Distributions of potential outcomes are derived from a large number of simulations (stochastic projections) which reflect the random variation in the inputs. The random variation is usually based on fluctuation observed in historical data for a selected period using standard time- series techniques.

A stochastic model is a tool for estimating probability distributions of potential outcomes by allowing or random variation in one or more inputs over time. A stochastic model is any model having some element of randomness. It refers to a variable process where the outcome has some degree of uncertainty. Stochastic meaning is being or having a random variable. Stochastic is a term originating from Greek stokhos, “a guess, aim”, is well described by a random probability distribution. Here’s a concise guide to make you understand the concept of stochastic in machine learning and how it is different from non-deterministic.īefore we proceed on to the concept of stochastic in machine learning, let’s first understand the stochastic meaning.

The concept of stochastic is important in machine learning algorithms and is to be understood properly to interpret the behaviour and performance of several machine learning algorithms effectively.