Sentence examples for likelihood in which from inspiring English sources

Secondly, we define an approximate likelihood, the "independence" likelihood, in which the correlations have been ignored between maximum intensity data of different durations.

For tracking a moving target, we mathematically model the task as searching a location sequence with the most likelihood, in which we first augment the probabilistic estimation learned in localization to construct the Emission Matrix and propose two human mobility models to approximate the Transmission Matrix in the Hidden Markov Model.

The method of estimation was full information maximum likelihood, in which we assumed that missing data were missing at random.

Variables were selected in a stepwise backward manner (maximum likelihood), in which the least significant variable was excluded at each step.

Bayesian maximum likelihood, in which one determines the parameter estimator by maximizing the posterior density, corresponds to a type of penalized least squares.

Shoot-level variation in number of vegetative leaves and basal leaf length was also tested in mixed models using maximum likelihood, in which population was treated as a fixed effect, and plant was included as a random effect.

The advantage of this approach is that participants with incomplete data can be included in the analyses, as Mx provides a method for handling incomplete data by using raw maximum likelihood estimation, in which a likelihood statistic (−2LL) of the data for each observation is calculated.

The models were fitted by means of restricted maximum likelihood estimation, in which the estimations of (restricted) log-likelihoods were based on the laplacian approximation.

Given a training set of enzymes and their catalytic residue annotations, we estimate the parameters (b, w 1, w 2) using a regularized maximum likelihood approach in which we maximize the sum of the likelihood and an L1 penalty term: where w =(w 1, w 2) and ‖ w ‖1=∑ k | w k | is the L1 norm.

The likelihood function, in which both data quantization and channel imperfection are considered, is calculated in three different scenarios.

In the importance step, one can address, say, signal fading or quantization, by evaluating a full likelihood function in which the corresponding occurrence probability has been incorporated.