Introduction
Sampling from distributions in statistics is a way of obtaining a subset of data from a larger population. The subset, or sample, is used to estimate some characteristics of the population, such as the mean, standard deviation, or proportion. A sampling distribution is the probability distribution of a statistic (such as the mean) derived from all possible samples of a given size from the same population. Sampling distributions help us understand how a sample statistic varies from sample to sample, and how likely it is to obtain a certain value of the statistic. Sampling distributions are essential for inferential statistics, as they allow us to perform hypothesis tests and calculate confidence intervals.
Contents
Inversion Sampling
Rejection Sampling
Importance Sampling
Gibbs Sampling
Metropolis-Hastings Algorithm
Reversible Jump MCMC