Sentence examples for optimize the log from inspiring English sources

A quadratic optimization problem was formulated, in which optimal w was sought to optimize the log likelihood for estimating variance components in REML.

The former methods typically involve building a position-weight matrix (PWM) from sequence data, and local search techniques such as expectation maximization or Gibbs sampling are used to optimize the log likelihood ratio until convergence or a maximum number of iterations is reached.

We can use numerical methods such as the quasi-Newton algorithm to numerically optimize the log-likelihood function given in Eq. (24), to get the maximum likelihood estimates of the parameters α,λ,a,b.

The latter can be written as (3) To optimize the log-likelihood, nonlinear optimisers such as scaled conjugate gradient (Nabney, 2001) can be used after having determined the gradient of the log-likelihood with respect to the latent points and the kernel parameters.

The results provide a promising prospect for designing and optimizing the log periodic nano-antenna in a broad range of wavelengths.

We can then learn a latent representation of the data X as well as the kernel hyperparameters by optimizing the log-likelihood.

As an alternative to optimizing directly the log-likelihood function (19), the EM algorithm [40] is well suited to solve optimization problems for mixture models and is simple to implement.

Beta coefficients were calculated by optimizing either the log-likelihood (NFS and CPSO) or a penalized maximum likelihood function (LASSO) through several iterations using bootstrap (NFS and CPSO) or a 10-fold cross-validation (LASSO) scheme [ 27, 28].

In Results and Discussion, we show that performance is optimized when using the log ratio of the mutant's read number to the total number of reads at each individual time point.

The network structure is determined by greedily optimizing the conditional log-likelihood: where Pr[ x] is flat (i.e. Pr[ x=0]=Pr[ x=1]=0.5) since we sample foreground and background positions equally.

The corresponding log-likelihood is By the same reason as (10), we optimize the penalized "complete data" log-likelihood where g y jk | B s) is the analogue of f̂ (y jk| B s).