Bazavov_BiasedMC - Biased Metropolis-Heatbath Algorithm...

Info iconThis preview shows pages 1–4. Sign up to view the full content.

View Full Document Right Arrow Icon

Info iconThis preview has intentionally blurred sections. Sign up to view the full version.

View Full DocumentRight Arrow Icon

Info iconThis preview has intentionally blurred sections. Sign up to view the full version.

View Full DocumentRight Arrow Icon
This is the end of the preview. Sign up to access the rest of the document.

Unformatted text preview: Biased Metropolis-Heatbath Algorithm Alexei Bazavov Florida State University November 2005 Introduction In the Metropolis procedure transition probability from the configuration ( k ) to ( l ) is given as W ( l )( k ) = f ( l, k ) w ( l )( k ) for l 6 = k W ( k ) ( k ) = f ( k, k ) + X l 6 = k f ( l, k )(1- w ( l )( k ) ) For the case of the non-symmetric proposal probabilities f ( l, k ) 6 = f ( k, l ) the acceptance probability can be modified as [Hastings (1970)] w ( l )( k ) b = min 1 , P ( l ) B P ( k ) B f ( k, l ) f ( l, k ) 1 Example: U (1) Lattice Gauge Theory Variables are complex numbers of unit length: U = e i , [0 , 2 ) The problem is reduced to sampling the probability density (PDF) P ( ) = N e cos where is a parameter associated to the interaction of the link being updated with its environment. The corresponding cumulative distribution function (CDF) is F ( ) = N Z d e cos where N ensures the normalization F (2 ) = 1....
View Full Document

Page1 / 13

Bazavov_BiasedMC - Biased Metropolis-Heatbath Algorithm...

This preview shows document pages 1 - 4. Sign up to view the full document.

View Full Document Right Arrow Icon
Ask a homework question - tutors are online