Sentence examples for I log l from inspiring English sources

These quantities are defined as AIC i = 2 k − log (L i ) AICc i = AIC i + 2 k (k + 1 ) n i − k − 1 BIC i = k log n i − n i log (L i ) where L i is the maximum of the individual likelihood function.

To make this connection, we parameterized the mean effect λ i, ξ j through the log-link function as: log (λ i, ξ j ) = μ ξ i + β ξ i log (L i ) + ζ ξ i log (K i ) where L i is the total length and K i is the coverage length for chromatin type i.

In this paper, G0 distribution is adopted [15, 16] so that the regional information function can be defined as r i G 0 x = - log p i G 0 ( x ; α i, γ i, L i ) = - log L i L i Γ L i - α i γ i α i Γ L i Γ - α i - L i - 1 log I x, i = 1, …, N + L i - α i log γ i + L i I x (10).

We obtain after multiplication by l i ( y k ) l i ( x k ) > 0 for each i = 1,..., p, l i ( y k ) l i ( x k ) - 1 ≤ l i ( y k ) l i ( x k ) log l i ( y k ) l i ( x k ).

(19) logWidth = log ((e − (i − 1 ) / n − e − (i + 1 ) / n ) / 2 ) (20) logW t i = logWidth + log L i (21) logZ = log.plus (logZ, logW t i ) (22) log L ∗ = log L i The stopping criterion requires the current l o g W t values to be compared with those of the posterior point identified 50 iterations earlier.

Assuming that neuronal responses can be described by Poisson statistics, we compute the log likelihood of s for the population response r i as following: log   L (s ) = ∑ i = 1 N log [ f i (s ) r i r i ! e x p (− f i (s ) ) ] = ∑ i = 1 N log (f i (s ) ) r i − ∑ i = 1 N f i (s ) − ∑ i = 1 N log (r i ! ) We can ignore the last term as it is stimulus s independent.

Corresponding values for the unpenalized likelihood, log L i ψ (Eq. 14), in the validation data were then obtained and accumulated across folds.

The models were fitted using maximum likelihood and compared using the likelihood ratio (LR) statistic, which is expressed as LR = 2 (log L(D) − log L(I)), where L(D) is the likelihood of the model that allows the traits to evolve in a correlated fashion and L(I) is the likelihood of the independent model.

Let l = log L be the log-likelihood.

The best simple model was identified using the Schwarz Information Criterion (SIC) (Schwarz 1978) as follows: S I C = − 2 log L + p log m o where L is the likelihood for the simple model, p is the number of parameters in the simple model, and m o is the number of observations (i.e., the 36 genotype means).

For the remainder we concentrate on log L = ∑ i ∑ j e ij ∑ t - 1 2 log σ 2 - 1 2 σ 2 (y it - α j - βt ) 2. Setting the partial derivative ∂ ∂ α j log L = 0 leads to α ^ j = ∑ i e ij ∑ t (y it - β t ) T ∑ i e ij.