The Short-Cut Metropolis Method
نویسنده
چکیده
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.
منابع مشابه
Supporting Information Network Topologies and Dynamics Leading to Endotoxin Tolerance and Priming in Innate Immune Cells
1. Detailed criteria for priming and tolerance in the Metropolis searching algorithm. 2. Two-stage Metropolis search for parameter sets that exhibit priming or tolerance. 3. Statistical method used to identify backbone motifs. 4. Motif density is more robust than frequency to variation in the topological cut-off. 5. 2D parameter correlations demonstrate how parameter compensation affects topolo...
متن کاملBayesian Methods for Cumulative , Sequential and Two - stepOrdinal
This paper considers the tting, criticism and comparison of three ordinal regression models { the cumulative, sequential and two-step models. EEcient algorithms based on Markov chain Monte Carlo methods are developed for each model. In the case of the cumulative model, a new Metropolis-Hastings procedure to sample the cut points is proposed. This procedure relies on a simple transformation of t...
متن کاملMCMC using Hamiltonian dynamics
Hamiltonian dynamics can be used to produce distant proposals for the Metropolis algorithm, thereby avoiding the slow exploration of the state space that results from the diffusive behaviour of simple random-walk proposals. Though originating in physics, Hamiltonian dynamics can be applied to most problems with continuous state spaces by simply introducing fictitious “momentum” variables. A key...
متن کاملApproximation Algorithm for the Max-Cut Problem
In this project, we investigated several approximation algorithms for the Max-Cut problem. Our main approach to this problem is a semideenite program (GW) based algorithm that has a performance guarantee of at least 0.878 of the optimal cut. We also show that we can perform exhaustive local search on top of the GW to enhance the result. Our results show that the running time of the local search...
متن کاملA Fast Algorithm for Simulated Annealing
We present a new deterministic algorithm for simulated annealing and demonstrate its applicability with several classical examples: the ground state energies of the 2d and 3d short range king spin glasses, the traveling salesman problem, and pattern recognition in computer vision. Our algorithm is based on a microcanonical Monte Carlo method and is shown to be a powerful tool for the analysis o...
متن کامل