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In statistics, Markov chain Monte Carlo (MCMC) is a class of algorithms used to draw samples from a probability distribution. Given a probability distribution, one can construct a Markov chain whose elements' distribution approximates it – that is, the Markov chain's equilibrium distribution matches the target distribution. The more steps that are included, the more closely the distribution of the sample matches the actual desired distribution.
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Search interest data over the past 12 months indicates that this topic periodically attracts global attention. Sudden spikes often correlate with major news events, public statements, or geopolitical developments.