Sentence examples for by minimizing the log-likelihood from inspiring English sources

A good fit to experiment 1 was obtained with the Bayesian inference model (see Fig. 3B, E, black line) by minimizing the log-likelihood of the subject data.

We can obtain β by minimizing the log-likelihood function of the logistic regression.

A non-linear pharmacodynamic concentration-response model was constructed, with parameters estimated by minimizing the log likelihood.

Then a genetic algorithm is used to obtain the optimal rate parameter values that minimize the log-likelihood function.

where and are estimated by minimizing the negative log-likelihood function using known training data and their decision values.

As mentioned before, the optimization for estimating the parameters can be performed by minimizing the negative log-likelihood and, for that, we use the nlminb function of the R language.

We used the gnlr function in the R software programme, which fits nonlinear regression equations to data for various common one and two parameter error distributions, including the binomial distribution, by minimizing the −log-likelihood via the Newton-Raphson iterative method, to fit both models in this study (see details in [23]).

We estimated parameters β0,…, β k from training data by minimizing the L2-regularized log-likelihood: 1 2 β T β + C ∑ i = 1 n log 1 + e − Rec i β T d i (4).

The parameters c j and d j are optimized by minimizing the global negative log-likelihood.

Then, for each sigmoid the parameters, c j and d j are optimized by minimizing the local negative log-likelihood: -sum_{k=1}^{N}{p_{k}text{log}(h_{k})+ 1-p_{k} text{log}(1-h_{k})}.

The p dimensional coefficient vector β = (β1,…, β p ) t can be estimated by minimizing the penalized negative log-likelihood: (2) 1 n ∑ i = 1 n − y i x i t β + log ⁡ 1 + exp ⁡ x i t β + ∑ j = 1 p J λ β j, where J λ is a penalty function and λ is a vector of tuning parameter that can be determined by a search on an appropriate grid.