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A drawback of most of the existing techniques is that the vector basis of the reduced space is built at an offline phase where the full system must be solved for a large sample of parameter values, which can also become highly time consuming.
Using MCMC a sample of parameter sets proportional to the probability density of that parameter set was obtained.
Instead, it aims to acquire a (potentially biased) sample of parameter points distributed all over the viable space.
The posterior density estimation from the adjusted sample of parameter values was carried out using the locfit function [ 60].
The parameter values used to simulate these selected datasets provide a sample of parameter values approximately distributed according to their own posterior distribution.
A representative sample of parameter sets was used to generate collections of stable sequences, and the most protein-like set was selected.
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Each replicate data set was simulated given an a posteriori drawn sample of parameters.
The output of an ABC algorithm is a sample of parameters from the distribution P(θ| d(D0, D* ≤ϵ).
In a first "global" step of their analysis, these techniques obtain a sample of parameters from the viable space, and then, in a "local" analysis, they study the local robustness around every element of this set.
The output of an ABC algorithm is a sample of parameters from the distribution which for sufficiently small ε is our approximation for the true posterior distribution, P(θ| D0).
Using Bayes' theorem, Monte Carlo (MC) or Markov Chain Monte Carlo (MCMC) methods are used to generate a sequence of samples of parameter values for each postulated model.
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CEO of Professional Science Editing for Scientists @ prosciediting.com