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Specifically, we can estimate the distributions parameters by maximizing the logarithm of the likelihood l of sample residuals.
The hyperparameter values Ω can thus be determined by maximizing the logarithm of the marginal likelihood [48, 52]: ln P ( y | Ω, X ) = − 1 2 y T C − 1 y − 1 2 ln | C | − n 2 ln 2 π (8).
The computational complexity can be further reduced by maximizing the logarithm of the p.d.f.. Hence, only (mathbf {h}_{ell ij}^{T}mathbf {Q} mathbf {h}_{ell ij},ii,j=1,2,ldots,S) should be calculated, requiring S 2×(d 2+d) multiplications and additions.
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Since the logarithm is an increasing function, it is the same maximizing the logarithm than the values inside it.
Maximizing the logarithm of a posteriori probability is equivalent to minimizing the ℓ1-regularized error function if Laplacian prior is applied.
By taking the logarithm, we have (15) We estimate the parameters by maximizing the log-likelihood function, l.
The optimum partition is determined by maximizing the LogWorth statistic (i.e., the negative base 10 logarithm of the p-value calculated from the sum of squares of the differences in means between the two groups formed by a partition [49]).
They responded by maximizing the number of lines they wrote.
Parameters are estimated by maximizing the likelihood of the model.
The model was fitted by maximizing the log-likelihood.
GEV parameters are calculated using the maximum likelihood method which maximizes the Logarithm of likelihood function given by L L = 1 n ∏ i = 1 n e v i ; ζ, δ, l = Σ i = 1 In n e v i ; ζ, 1 Open image in new window (7).
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