pub struct ReesWalker<R, Hist, Energy, S, Res> { /* private fields */ }
Expand description

Walker for Replica exchange entropic sampling

  • performes the random walk in its respective domain

Implementations

Returns id of walker
  • important for mapping the ensemble to the walker

Returns reference of current energy

Returns current energy

fraction of how many replica exchanges were accepted and how many were proposed

  • Old non normalized estimate of the natural logarithm of the probability density function
  • for refined density use self.log_density_refined()
  • Current non normalized estimate of the natural logarithm of the probability density function
  • calculated by refining old density with current histogram
How does the refining work?
  • Let P(i) be the current probability density function (non normalized) at some index i
  • Let H(i) be the histogram at some index i We will now calculate the refined density P’, which is calculated as follows:

P’(i) = P(i) * H(i) (if H(i) != 0)

P’(i) = P(i) (if H(i) == 0)

Or in log space, which is what is actually calculated here:

ln(P’(i)) = ln(P(i)) + ln(H(i)) (if H(i) != 0)

ln(P’(i)) = ln(P(i)) (if H(i)=0)

for more information see

J. Lee, “New Monte Carlo algorithm: Entropic sampling,” Phys. Rev. Lett. 71, 211–214 (1993), DOI: 10.1103/PhysRevLett.71.211

Old estimate of log10 of probability density
  • normalized (sum over non log values is 1 (within numerical precision))
Current estimate of log10 of probability density
  • normalized (sum over non log values is 1 (within numerical precision))
is the simulation finished?
  • true, if more (or equal) steps than the step threshold are performed
Refine current probability density estimate

Trait Implementations

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