Adaptive metropolis refers to an advanced version of the Metropolis-Hastings algorithm that adjusts the proposal distribution based on previous samples to improve the efficiency of the sampling process. This approach allows for more effective exploration of the target distribution by dynamically tuning the parameters of the proposal distribution, thus reducing the autocorrelation of samples and leading to faster convergence.
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