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To overcome this limitation we define the subspace W via a family of ellipsoids that cover the viable space locally (do not confuse with the ellipsoid based exploration of the viable space described above).
A novel method we call multiple ellipsoid based sampling (MEBS) carries out this fine-grained exploration of the viable space.
We next describe our coarse-grained, global exploration of the viable space via an out-of-equilibrium adaptive Metropolis Monte Carlo sampling (OEAMC).
The exploration of the viable space allows us to identify a (typically nonconvex) subspace of the whole parameter space in which the proportion of viable parameter points is much higher than in the whole space.
The first step of the algorithm consists of a global coarse-grained exploration of the viable space by an out-of-equilibrium adaptive Monte Carlo (OEAMC) sampling of the entire parameter space.
In a second step, we therefore carry out a fine-grained exploration of the viable regions already identified through OEAMC, using a technique we call multiple ellipsoid-based sampling (MEBS).
This methodology produces a two-dimensional map representation of the, usually, high dimensional input space, along with quantitative information on viable clustering alternatives, which facilitates the exploration of the possible partitions in a dataset.
This permits a ready exploration of the space by ellipsoid expansions - efficient "travel" of ellipsoids inside the viable volume is possible.
Increases in the mutation rate of quasispecies as a natural strategy to optimize the exploration of the sequence space have been also documented through the description of hypermutated (though still viable) viral genomes in in vivo infections (reviewed in [ 33]).
Like an exploration of the Paris catacombs.
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Justyna Jupowicz-Kozak
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