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Figure 6 shows the steady solution curve under the most likely parameters (ω = 3.8, R c = 16 m), based on the discussion in the previous section.
It is believed that this is implemented in cortical areas by elementary inferential computations dynamically extracting the most likely parameters corresponding to the sensory signal.
The training is accomplished using the Baum-Welch algorithm [41], an expectation maximization technique that finds the most likely parameters for an HMM given a training set of emissions.
The most likely parameters for an individual can be obtained using measurements of drug concentrations [ 16, 17] or drug responses [ 18, 19].
Utilising immunohistochemistry and FISH, we retrospectively investigated the most likely parameters with immediate impact on HER2 activity and Herceptin™ responsiveness in a cohort of 48 HT breast cancer patients with HER2 amplification.
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We computed the flux for females and males using the 1,000 most likely parameter configurations for each of the 8 populations.
Instead of taking the ratio of sampling models under each hypothesis evaluated at the most likely parameter value (MLE) of each as in the Frequentist paradigm, the BF is the ratio of the sampling models averaged over their respective prior distributions on the model parameters.
This bias occurs because the maximum likelihood estimator selects the most likely parameter value (the mode of the likelihood function), and not the expected value of the parameter.
The most representative approach of the first class is maximum-likelihood estimation (ML), according to which the most likely parameter values are the ones that maximize the probability (likelihood) of observing the given data.
This procedure estimates the most likely SA parameters, given the model and observed data.
We used multivariate analysis of bioclimatic and edaphic variables across all sites (see Environmental distance matrix) to identify the most likely environmental parameters distinguishing these populations.
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