- Referenced in 1461 articles
- functional data analysis functions and sample analyses through the CRAN distribution system. This ... files containing the functions and sample analyses, as well as two .txt files giving instructions...
- Referenced in 697 articles
- TSPLIB is a library of sample instances for the TSP (and related problem) from various...
- Referenced in 430 articles
- about the true structure in the large sample limit, provided that structure and the sample...
- Referenced in 426 articles
- resampling methods for use with single samples from parametric and nonparametric models. Chapter 3 extends ... basic ideas to several samples, semiparametric and smooth models. Significance and confidence intervals...
- Referenced in 228 articles
- Iterative signal recovery from incomplete and inaccurate samples. Compressive sampling offers a new paradigm ... basis. The major algorithmic challenge in compressive sampling is to approximate a compressible signal from ... noisy samples. This paper describes a new iterative recovery algorithm called CoSaMP that delivers ... requires only matrix-vector multiplies with the sampling matrix. For compressible signals, the running time...
- Referenced in 286 articles
- Stan: A C++ Library for Probability and Sampling. Stan is a probabilistic programming language implementing ... full Bayesian statistical inference with MCMC sampling (NUTS, HMC) and penalized maximum likelihood estimation with...
- Referenced in 393 articles
- surface and improving the approximation by sampling where the prediction error may be high...
- Referenced in 378 articles
- BUGS (Bayesian inference Using Gibbs Sampling) project is concerned with flexible software for the Bayesian...
- Referenced in 298 articles
- intended usage. Please read our sample license agreement (or the german version) for more details...
- Referenced in 293 articles
- conditional density estimation under a variety of sampling schemes; and (iii) hazard rate estimation with...
- Referenced in 149 articles
- SMOTE: Synthetic Minority Over-sampling Technique. An approach to the construction of classifiers from imbalanced ... cost of the reverse error. Under-sampling of the majority (normal) class has been proposed ... combination of our method of over-sampling the minority (abnormal) class and under-sampling ... performance (in ROC space) than only under-sampling the majority class. This paper also shows...
- Referenced in 250 articles
- Allows for witness set manipulation via both sampling and membership testing. Accepts square or nonsquare...
- Referenced in 208 articles
- effects Models in S .Data sets and sample analyses from Pinheiro and Bates, ”Mixed-effects...
- Referenced in 196 articles
- penalised likelihood , survival analysis: descriptive statistics, two-sample tests, parametric accelerated failure models, Cox model...
- Referenced in 131 articles
- some of the algorithms it incorporates. Four sample CAYLEY programs are given: a test ... easy and comfortable to use. As the sample programs show, novel approaches may be needed...
- Referenced in 127 articles
- have to be done. Note, that all sample codes given in [R. Klatte ... work properly with C-XSC 2.0. Sample codes are available on the web page...
- Referenced in 167 articles
- package ’nacopula’ for nested Archimedean copulas: Efficient sampling algorithms, various estimators, goodness-of-fit tests...
- Referenced in 120 articles
- evolution strategy, new candidate solutions are sampled according to a multivariate normal distribution ... candidate solutions is exploited for learning the sample distribution and neither derivatives nor even...
- Referenced in 110 articles
- about which we have phaseless samples of the form ... knowledge of the phase of these samples would yield a linear system). This paper develops...
- Referenced in 152 articles
- network is trained on batches of randomly sampled time and space points. The algorithm...