Sentence examples for likelihood problems from inspiring English sources

Cheng [4] showed that in unbounded likelihood problems such as estimation of three-parameter gamma, lognormal or Weibull distributions, the MSP estimation method produces consistent and asymptotically efficient estimators.

Also, Cheng [4] showed that in unbounded likelihood problems such as estimation of three-parameters in the Weibull distribution, the MSP estimation method produces consistent and asymptotically efficient estimators.

Because of initial likelihood problems at the start of the analyses we used a starting tree obtained from the Bayesian analysis of Massoni et al. [ 33] and rendered ultrametric using Penalized Likelihood (PL) implemented in r8s v1.8 [ 91].

The model is presented as a maximum likelihood problem involving discrete variables.

The demapping task is often posed as a maximum likelihood problem [6].

The proposed GIR algorithm conducts motion correction by solving the maximum likelihood problem.

The distribution can be estimated by formulating a maximum likelihood problem [30, 31], where the parameters in a family of distributions are optimized to fit the data.

We proposed an alternating minimization (AM) algorithm based on turning a maximum likelihood problem into a double minimization of I-divergence introduced by Csiszár [13].

For a sequence of states,, and frame parameters (where is a vector containing both the position and velocity at time ) this can be formulated as a maximum likelihood problem: (4).

Specifically, two steps can be taken to solve the maximum likelihood problem.

Because the gradient of the likelihood function is not directly available, SML uses a genetic algorithm to solve the maximum likelihood problem.