A sequential sampling algorithm or adaptive sampling algorithm is a sampling algorithm that obtains instances sequentially one by one and determines from these instances whether it has already seen enough number of instances for achieving a given task.
Scientists at a workshop held recently by the Human Genome Project, for instance, discussed the prospects of achieving the "thousand-dollar genome".
He says that neither laypersons nor philosophers can articulate a relevant distinction, because each instance satisfies the utilitarian standard of achieving the greatest good for the greatest number and that the scenarios are morally equivalent.
Given that 60% of the participants surveyed felt that they did not have sufficient time to care for an advanced cancer patient, the use of dedicated PC staff integrated into the ED may, in some instances, be an important way of achieving optimal quality of care.
These early results show encouraging symptomatic and objective responses of gastro-oesophageal carcinoma to ECF, but provide no instances of ECF achieving complete pathological response.
Will it lead, for instance, to greater differentiation, which will increase the problems of achieving integration?
For instance, to achieve the training of more fine-grained arm movements, the therapist can customize the parameters of the lifting exercise to a higher difficulty.
For instance, to achieve an accuracy of no less than 90% in the paired-end sequencing, around 50x coverage may be needed, with a safer choice of about 100x coverage.
For instance, to achieve a W of 0.20, a journal subset with at least 25 pass articles for the treatment category would be needed to update search strategies; for the diagnosis and prognosis categories, journal subsets with at least 88 and 96 pass articles are needed, respectively.
For instance, to achieve the same accuracy of volume estimation in d = 15 dimensions, the combination of MEBS and OEAMC used approximately 2-fold smaller samples than the OEAMC alone (and 17 order of magnitude smaller samples than uniform sampling).
