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Figure 1.
Fig. 1. Replica-exchange Monte Carlo algorithm. (A) We generate
a stochastic sample (X(k),  (k),  (k)) from the
joint posterior distribution in an iterative fashion by using
Gibbs sampling (20). The nuisance parameters and are consecutively
drawn from their conditional posterior distributions, with the
values of the other parameters being fixed to their previously
generated values. Coordinates are updated by using the hybrid
Monte Carlo method (21). (B) To overcome energy barriers, we
embed this scheme in a replica-exchange strategy, which
simulates a sequence of heated copies of the system. Samples of
the target distribution are generated in the low-temperature
copy (T[low]) and propagate via stochastic exchanges between
intermediate copies (T[low] < T[i] < T[high]) to the
high-temperature system (T[high]). The temperature T[high] is
chosen such that the polypeptide chain can move freely in order
to escape local modes of the probability density.
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