A unified framework for time-changed Markov processes shows how to accelerate MCMC convergence while preserving the target distribution, unifying several known algorithms.
Applying the generator of Y to ¯V we find eL ¯V(z) = 1 (1 + V(z))2 ⟨ ˜Φ(z), ∇V(z)⟩ + ˜λ(z) Z ( ¯V(y) − ¯V(z)) eQ(z, dy)
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Sampling with time-changed Markov processes
A unified framework for time-changed Markov processes shows how to accelerate MCMC convergence while preserving the target distribution, unifying several known algorithms.