| |
|
|
|
|
|
|
|
|
1. |
Record Nr. |
UNISA996387124403316 |
|
|
Autore |
Naylor James <1617?-1660.> |
|
|
Titolo |
A discovery of faith, wherein is laid down the ground of true faith [[electronic resource] ] : which sancifieth and purifieth the heart, and worketh out the carnal part, shewing the way that leadeth to salvation : with the difference betwixt the two seeds, the one of Mount Sinai, which tendeth to bondage, and the other, which is the immortal seed of God, begotten by the immortal word, which liveth and abideth forever |
|
|
|
|
|
|
|
Pubbl/distr/stampa |
|
|
London, : Printed for Giles Calvert ..., 1653 |
|
|
|
|
|
|
|
Descrizione fisica |
|
|
|
|
|
|
Soggetti |
|
|
|
|
|
|
Lingua di pubblicazione |
|
|
|
|
|
|
Formato |
Materiale a stampa |
|
|
|
|
|
Livello bibliografico |
Monografia |
|
|
|
|
|
Note generali |
|
Reproduction of original in Huntington Library. |
Attributed to James Nayler. cf. NUC. |
Signed: "James Nayler. A prisoner at appleby in Westmorland for the truths sake." |
|
|
|
|
|
|
|
|
Sommario/riassunto |
|
|
|
|
|
|
|
|
|
|
|
|
|
2. |
Record Nr. |
UNINA9910404091103321 |
|
|
Autore |
Kim Jong-Min |
|
|
Titolo |
Uncertainty Quantification Techniques in Statistics |
|
|
|
|
|
Pubbl/distr/stampa |
|
|
MDPI - Multidisciplinary Digital Publishing Institute, 2020 |
|
|
|
|
|
|
|
ISBN |
|
|
|
|
|
|
Descrizione fisica |
|
1 online resource (128 p.) |
|
|
|
|
|
|
Lingua di pubblicazione |
|
|
|
|
|
|
Formato |
Materiale a stampa |
|
|
|
|
|
Livello bibliografico |
Monografia |
|
|
|
|
|
Sommario/riassunto |
|
Uncertainty quantification (UQ) is a mainstream research topic in applied mathematics and statistics. To identify UQ problems, diverse modern techniques for large and complex data analyses have been developed in applied mathematics, computer science, and statistics. This Special Issue of Mathematics (ISSN 2227-7390) includes diverse modern data analysis methods such as skew-reflected-Gompertz information quantifiers with application to sea surface temperature records, the performance of variable selection and classification via a rank-based classifier, two-stage classification with SIS using a new filter ranking method in high throughput data, an estimation of sensitive attribute applying geometric distribution under probability proportional to size sampling, combination of ensembles of regularized regression models with resampling-based lasso feature selection in high dimensional data, robust linear trend test for low-coverage next-generation sequence data controlling for covariates, and comparing groups of decision-making units in efficiency based on semiparametric regression. |
|
|
|
|
|
|
|
| |