Computational statistics

The view that the broader concept of computing must be taught as part of general statistical education is gaining momentum.

[4] In 1908, William Sealy Gosset performed his now well-known Monte Carlo method simulation which led to the discovery of the Student’s t-distribution.

[6][7] Later on, the scientists put forward computational ways of generating pseudo-random deviates, performed methods to convert uniform deviates into other distributional forms using inverse cumulative distribution function or acceptance-rejection methods, and developed state-space methodology for Markov chain Monte Carlo.

[9] The development of these devices were motivated from the need to use random digits to perform simulations and other fundamental components in statistical analysis.

Monte Carlo is a statistical method that relies on repeated random sampling to obtain numerical results.

Monte Carlo methods are mainly used in three problem classes: optimization, numerical integration, and generating draws from a probability distribution.

The Markov chain Monte Carlo method creates samples from a continuous random variable, with probability density proportional to a known function.