The Short-Cut Metropolis Method
Radford M. Neal
Abstract
I show how one can modify the random-walk Metropolis MCMC method in such a way that a sequence of modified Metropolis updates takes little computation time when the rejection rate is outside a desired interval. This allows one to effectively adapt the scale of the Metropolis proposal distribution, by performing several such "short-cut" Metropolis sequences with varying proposal stepsizes. Unlike other adaptive Metropolis schemes, this method converges to the correct distribution in the same fashion as the standard Metropolis method.
Create a lesson
Related papers
On Natural Exponential Families with Log-Affine Generalized Variance
Zeyu Cao, Shaosai Huang
Fundamental Limits of Coherent Imaging in The Presence of Atmospheric Turbulence
Songbin Liu, Hanyu Yang, Junren Chen et al.
Regularization vs. Convolution in Kernel-Based Hypothesis Testing
Omar Hagrass, Bharath K. Sriperumbudur
Inference for Realized Quadratic Covariation of R and R2 as a Measure of Return Asymmetry
Haozhe Jiang, Ostap Okhrin, Michael Rockinger
Variational Splines and Generalized Gaussian Processes
Michael Unser
Conformal Prediction at Performative Equilibrium: The Price of Performativity
Ahmed Mahrous, Roberto Di Pietro