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13 Finite sample variability should not be a major concern here given the large number of observations (on average above 1,000) which are used to estimate the mode of each occupation.
Given an observed sequence O, we estimate the mode of the posterior distribution as follows: ϕ = argmax z ∏ c = 1 C ∏ t = 1 N c N o t | ℳ c + D c z c, Φ c N z c | 0, I. (6).
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Many algorithms have been developed to estimate the modes of free oscillations in a power system.
The (Δ k) statistic derived from the second order rate of change of the likelihood function (Evanno et al. 2005) provided an improved estimate of the mode of true (k), and the mean Q values for each population were established using the full search algorithm in CLUMPP (Jakobsson and Rosenberg 2007).
The estimates are the mode of the posterior distribution with the 90% HPD (Highest Posterior Density) interval between parentheses.
The genetic variance was estimated as the mode of its approximate posterior density after Laplace integration of the other parameters [ 13].
Then, for the fixed maximizing hyper-parameters, the maximized solution of the penalized log-likelihood in (3) is nothing but the optimal maximum posterior estimate, i.e., the mode of the posterior density.
We model the background distribution as a Gaussian and estimate its mean as the mode of the mixture distribution.
A density-based clustering algorithm is introduced to estimate the mode shapes even when the number of measured responses is less than the number of active modes.
Indeed, the best fitting estimates were close to the mode of the distribution, which was somewhat different from the mean.
However, the Predict estimates did not utilise the mode of detection component of the model as the mode of detection was not available for the British Columbia cohort.
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