Sentence examples for data augmentation methods from inspiring English sources

Third, the missing data problems, which are one major concern of this survey due to their universality in networked systems, have been tackled by researchers using certain forms of MCMC method, such as the Gibbs sampler [10] and other data augmentation methods.

Model fitting and parameter estimation utilized the Markov-Chain Monte-Carlo algorithm [ 24], and interval censored data were addressed using data augmentation methods [ 25].

Here we show that data augmentation methods allow these problems to be overcome by treating the unobserved times at which patients acquire MRSA as unknown parameters to be estimated.

Moreover, to address the problems with small samples, a data augmentation method was proposed.

In order to mitigate the overfitting issue on small-scale dataset, a video data augmentation method is used to increase the amount of training data, as well as a two-step training strategy is adopted to train our recurrent hybrid network.

Bootstrap is a Monte Carlo-type data augmentation method utilizing resampling with replacement that can be used with observed data.

For implementing the Gibbs sampling procedure to estimate parameters and to predict state variables, we use data augmentation method to expand the model.

Since the likelihood of the observed data (dates and outcomes) given the model parameters is computationally intractable, we used a data augmentation method in which the unobserved colonization times were included as additional parameters.

We tested for heterogeneity by outcome strata (that is, low-grade vs high-grade tumours; localised vs advanced tumours) using the data augmentation method by Lunn and McNeil (1995).

Differences in risk estimates of a given factor and across breast cancer subtypes were analyzed using the data augmentation method as described by Lunn and McNeil, using a likelihood ratio test to compare the model with and without interaction terms between the exposure of interest and breast cancer subtype [ 23].

Differences between BMI, waist and hip circumferences and risk of breast cancer subtypes were analyzed using the data augmentation method as described by Lunn and McNeil, using a log likelihood ratio test to compare the model with and without interaction terms between the anthropometric variable and breast cancer subtype [ 27].