Summary

• Good Monte Carlo keeps the system moving among a wide variety of states

• At times sampling of wide distribution is not done well

  • many states of comparable probability not easily reached
  • few states of high probability hard to find and then escape

• Biasing the underlying transition probabilities can remedy problem

  • add bias to underlying TPM
  • remove bias in acceptance step so overall TPM is valid

• Examples

  • insertion/deletion bias in GCMC
  • force bias
  • association bias
  • using an approximate potential

Previous slide

Back to first slide

View graphic version