This contribution is part of the special series of Inaugural Articles by members of the National Academy of Sciences elected in 2017. Edited by Kenneth A. Ribet, University of California, Berkeley, CA ...
Linear regression is the most fundamental machine learning technique to create a model that predicts a single numeric value. One of the three most common techniques to train a linear regression model ...
scdhlm provides a set of tools for estimating hierarchical linear models and effect sizes based on data from single-case designs. The estimated effect sizes, as described in Pustejovsky, Hedges, and ...
Dipartimento di Chimica, Università di Torino, via Giuria 5, 10125 Torino, Italy ...
In the past few decades, multi-linear algebra also known as tensor algebra has been adapted and employed as a tool for various engineering applications. Recent developments in tensor algebra have ...
Ordinary differential equation (ODE) models are frequently applied to describe the dynamics of signaling in living cells. In systems biology, ODE models are typically defined by translating relevant ...
Randomization isn’t just used to model noise in data; it can be a powerful computational resource to develop algorithms with improved running times and stability properties as well as algorithms that ...
Some results have been hidden because they may be inaccessible to you
Show inaccessible results