Karger, D.: Random sampling and greedy sparsification for matroid optimizaiton problems. Mathematical Programming 82, 41–81 (1998) MathSciNetD. R. Karger, "Random sampling and greedy sparsification for matroid optimization problems," Mathematical Programming, vol. 82, no. 1-2, pp. 41-81, ...
We propose a new multi-fidelity surrogate model to speed up the sampling procedure while maintaining high accuracy. Our numerical simulations demonstrate that this method not only yields high-quality reconstructions but also substantially reduces...
0.95 ### attempts to fix things with a bit of manual work ### ### manual approach, which ensures that data is kept randomg for the imputed bits ### prep_post <- prepare_predictions(fit) #> Error: Argument 'resp' must be a single variable name for models using addition argument ...
You can think of the inference model as linked to the sampling model for blocks If the blocks observed are a (random) sample of blocks, then they are a source of random variation If blocks observed are the entire universe of relevant blocks, then they are not a source of random variation...
Mathé P, Pereverzev SV (2006) Regularization of some linear ill-posed problems with discretized random noisy data. Math Comput 75(256):1913–1929 MATHGoogle Scholar Michel V (2014) Tomography: problems and multiscale solutions. In: Freeden W, Nashed MZ, Sonar T (eds) Handbook of geomathe...
The estimates for each country were also combined into a random-effect meta-analysis with the Higgins’s I2 statistic being calculated. Higgins’s I2 represents the degree of heterogeneity between countries that is not explained by sampling error with a value of 25% often considered as low, ...
sippi_AM13_metropolis_gaussian.m: Metropolis sampling using Gaussian prior sippi_AM13_metropolis_bimodal.m: Metropolis sampling using Gaussian prior / bimodal distribution sippi_AM13_metropolis_uniform.m: Metropolis sampling using Gaussian prior / uniform distribution sippi_AM13_rejection_gaussian.m: Rej...
The sample chosen by random under sampling may be a biased sample. And it will not be an accurate representative of the population. Thereby, resulting in inaccurate results with the actual test data set. 2.1.2 Random Over-Sampling Over-Sampling increases the number of instances in the minority...
The constant ϵ, which is set to 0.1, determines the noise level as it is multiplied with the standard deviation of the input field. G(0,1) is a random value which is sampled from the standard normal distribution with mean and standard deviation of zero and one, respectively. ...
Undersampling is a widely adopted method to deal with imbalance pattern classification problems. Current methods mainly depend on either random resampling on the majority class or resampling at the decision boundary. Random-based undersampling fails to take into consideration informative samples in the ...