Adaptive Metropolis Sampling and Optimization with Product Distributions
نویسندگان
چکیده
The Metropolis-Hastings (MH) algorithm is a way to IID sample a provided target distribution π(x). It works by repeatedly sampling a separate proposal distribution T (x, x′) to generate a random walk {x(t)} which converges to a set of samples of π. Here, we introduce a T -updating phase after the cooling period and before sampling begins. In the updating phase, {x(t)} is used to update T at t and our update method corresponds to the information-theoretically optimal meanfield approximation to π. We employ our algorithm to sample the energy distribution for several spin-glasses and we demonstrate the superiority of our algorithm to the conventional MH algorithm. [email protected] [email protected]
منابع مشابه
Adaptive Metropolis Sampling with Product Distributions
The Metropolis-Hastings (MH) algorithm is a way to sample a provided target distribution π(x). It works by repeatedly sampling a separate proposal distribution T (x, x) to generate a random walk {x(t)}. We consider a modification of the MH algorithm in which T is dynamically updated during the walk. The update at time t uses the {x(t < t)} to estimate the product distribution that has the least...
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