The robust bootstrap is a computer-intensive inference method for robust regression estimators which is computationally simple (because we do not need to re-compute the robust estimate with each bootstrap sample) and robust to the presence of outliers in the bootstrap samples.
Estuarine geomorphology worldwide has greatly changed in the Anthropocene due to intensive human inferences in river basin and within estuary, which has received increasing global concerns.
Today, computer-intensive approaches for parameter inference, such as Bayesian Markov chain Monte Carlo (Sorensen & Gianola 2002; Gelman et al. 2004) or approximate Bayesian computation (Beaumont et al. 2002), can be implemented effectively in today′s high throughput systems (Wu et al. 2011).
We compare memory intensive AND/OR graph search with inference methods, and place various existing algorithms within the AND/OR search space.
Despite the simplicity of these models, inference is computationally intensive given the high number of regressors; expression data on 1000 10 000s of genes is typical depending on the experimental conditions and organism, and potentially greater if gene models (RNA splicing/transcripts) are distinguished.
With the increasing availability of computational power, numerically-intensive statistical methods for parameter inference have become more accessible.
The objective is to reduce the fuzzy rules and make the fuzzy inference set less computationally intensive and fast, as well as exploiting the advantages of easy trainability and high generalizability.
In spite of some attempts to reduce the computational cost [ 27], the Bayesian network approach in general is computationally intensive to implement, especially for network inference on a genome-wide scale.
Intensive attempts are made to draw inferences based on small case groups through the application of extremely sophisticated and informative spatial methods at the level of neighborhoods or households.
This step of phylogenetic inference is imperfect and computationally intensive, and by side-stepping phylogenetic reconstruction, we arrive at genealogies quickly and accurately.
Immgen is the data-intensive project where the most advanced network inference methodology has been applied.
