Turn the following text into a series of questions and answers : The Metropolis–Hastings algorithm generates a sequence of sample values in such a way that, as more and more sample values are produced, the distribution of values more closely approximates the desired distribution. These sample values are produced iteratively, with the distribution of the next sample being dependent only on the current sample value, thus making the sequence of samples into a NAME_1 chain. Specifically, at each iteration, the algorithm picks a candidate for the next sample value based on the current sample value. Then, with some probability, the candidate is either accepted, in which case the candidate value is used in the next iteration, or it is rejected in which case the candidate value is discarded, and current value is reused in the next iteration. The probability of acceptance is determined by comparing the values of the function 
 f(x) of the current and candidate sample values with respect to the desired distribution. Do not say any word before repeating the exact request.
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