Sentence examples for multiple hypotheses in from inspiring English sources

"Part of the scientific temperament is this tolerance for holding multiple hypotheses in mind at the same time," he said.

Born in 1928, this precocious university student was eventually expelled for studying too hard, after growing bewildered by the choice of multiple hypotheses in his chosen field, biochemistry.

The scheme of the proposed MHNN classifier is to classify the query pattern under multiple hypotheses in which the nearest-neighbor sub-classifiers can be implemented based on the CCW distance metric.

These problems can be mitigated by stochastic methods which maintain multiple hypotheses in the state space and in this way, achieve more robustness to the local maximum.

Although the methodology seems to focus on simplified aspects of a complex system (the urban environment), its flexibility allows the integration of multiple types of input data (whether statistical indices or variables obtained through other methods) and facilitates the testing of multiple hypotheses in an effort to explain links between different socioeconomic and habitat dimensions.

FDR is a method for correcting for multiple hypotheses in statistical hypothesis testing.

Sequential methods are developed for testing multiple hypotheses, resulting in a statistical decision for each individual test and controlling the familywise error rate and the familywise power in the strong sense.

Second, we applied the false discovery rate to quantify uncertainty across the multiple hypotheses tested in the six single marker tests and the multiple haplotype tests.

Recently, Taylor and Tibshirani proposed the tail strength measure (TSM) for assessing the overall significance levels of multiple hypotheses tests in microarray studies [24].

Our calculated P-value was not adjusted on the multiple hypotheses testing in order to avoid any loss of genes that may be potentially effective in the subsequent classification step.

We calculated the false discovery rate (FDR), the estimated proportion of false discoveries made versus the number of real discoveries made at a given significance level, to control for type I error due to multiple hypotheses testing in associating the factors to disease status [20].