Turn the following text into a series of questions and answers : In statistics and statistical physics, the Metropolis–NAME_1 algorithm is a NAME_2 chain NAME_3 (MCMC) method for obtaining a sequence of random samples from a probability distribution from which direct sampling is difficult. This sequence can be used to approximate the distribution (e.g. to generate a histogram) or to compute an integral (e.g. an expected value). Metropolis–NAME_1 and other MCMC algorithms are generally used for sampling from multi-dimensional distributions, especially when the number of dimensions is high. For single-dimensional distributions, there are usually other methods (e.g. adaptive rejection sampling) that can directly return independent samples from the distribution, and these are free from the problem of autocorrelated samples that is inherent in MCMC methods. There should be exactly 6 paragraphs separated by the markdown divider: ***.
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