Introduction This article outlines the research protocol for a multicentre, randomised, controlled study designed to evaluate the therapeutic effect of a modified olfactory training (MOT) based on ...

Learn how acceptance sampling improves quality control by evaluating random samples. Discover its methods, benefits, and historical significance in manufacturing.

It stands to reason that if you have access to an LLM’s training data, you can influence what’s coming out the other end of the inscrutable AI’s network. The obvious guess is ...

Eeny, meeny, miny, mo, catch a tiger by the toe – so the rhyme goes. But even children know that counting-out rhymes like this are no help at making a truly random choice. Perhaps you remember when ...

Sankhyā: The Indian Journal of Statistics, Series B (2008-), Vol. 78, No. 1 (May 2016), pp. 66-77 (12 pages) We consider the problem of unbiased estimation of a finite population mean (or proportion) ...

Forbes contributors publish independent expert analyses and insights. Dr. Lance B. Eliot is a world-renowned AI scientist and consultant. In today’s column, I examine a newly revealed technique in ...

Mailing human papillomavirus (HPV) self-sampling kits to people due for cervical cancer screening was more cost effective than usual care, which included clinician electronic medical record alerts and ...

Objective To perform a large-scale pairwise and network meta-analysis on the effects of all relevant exercise training modes on resting blood pressure to establish optimal antihypertensive exercise ...

Abstract: In this paper, the paradigm of the traditional iterative decoding schemes for the uplink large-scale MIMO detection is extended by sampling in an Markov chain Monte Carlo (MCMC) way.

Patients undergoing cardiac surgery often receive red-cell transfusions, along with the associated risks and costs. Early intraoperative normovolemic hemodilution (i.e., acute normovolemic ...

In statistics and machine learning, logistic regression is a widely-used supervised learning technique primarily employed for binary classification tasks. When the number of observations greatly ...