The world of cells is surprisingly noisy. Each cell carries unique genetic information, but when we try to measure cellular ...

Machine learning and artificial intelligence wouldn't be possible without the statistical models that underpin their analytic capabilities. A Cornell statistician and his colleague have developed a ...

Distance multivariance is a multivariate dependence measure, which can detect dependencies between an arbitrary number of random vectors each of which can have a distinct dimension. In his new article ...

The research, published in Physical Review Research, introduces a new method based on statistical mechanics to improve the discovery of equations directly from noisy real-world data. Statistical ...

Various statistical forecasting methods exist designed for use with slow-moving products, new product introductions, stable mature products and products with erratic demand. Determining which ...

An international team of mathematicians, led by Lehigh University statistician Taeho Kim, has introduced an innovative method ...

The efficiency of Monte Carlo simulations is significantly improved when implemented with variance reduction methods. Among these methods, we focus on the popular importance sampling technique based ...

“One Guinness, please!” a customer says to a barkeep, who flips a branded pint glass and catches it under the tap. The barkeep begins a multistep pour process lasting precisely 119.5 seconds, which, ...

Researchers from Peking University have conducted a comprehensive systematic review on the integration of machine learning into statistical methods for disease risk prediction models, shedding light ...