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A new sample with attribute values would be classified into the most probable class based on posterior probability and computed according to the Bayes theorem [31].
Firstly, classification based on posterior probability allows assessment of the quality of classification.
Parameter estimates were based on posterior probability distributions constructed by sampling the stationary distribution for 40,000,000 generations, sampling every 1000 steps.
In the Bayesian statistical framework, the inferences are based on posterior probability distributions of the quantities of interest (Gelman et al., 2004).
A site-specific profile based on posterior probability (Qk) was used to predict critical amino acid residues that were responsible for functional divergence.
In root development, we found that datasets containing experiments done on the root contributed over 2 times more information (based on posterior probability, Figure 5) than the same experiments done on the shoots.
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On ten benchmark datasets, we observe that the two decoding strategies based on posterior probabilities in this paper obtain better performance than other ones in earlier references.
Passerini et al. (2004) [16] have recently proposed a decoding strategy based on posterior probabilities.
It requires specifying ecosystem states that describe conditions for ecosystem services, and decides which hypothesis is true based on posterior probabilities of ecosystem states.
At each resolution, coefficients associated with noise are modelled by Gaussian random variables; coefficients associated with edges are modelled by Generalized Laplacian random variables, and a shrinkage function is assembled based on posterior probabilities.
Class assignment was based on posterior probabilities.
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