A paper published in Biology Methods and Protocols, indicates that a new computational method may help researchers identify ...

Latent variable modeling comprises a suite of methodologies that infer unobserved constructs from observable indicators, thereby enabling researchers to quantify abstract phenomena across diverse ...

Extending the traditional discrete choice model by incorporating latent psychological factors can help to better understand the individual's decision-making process and therefore to yield more ...

This paper gives a matrix approach to determine when a statistical dependence between two manifest categorical random variables can be viewed as generated by some unobserved latent variables, in the ...

In this talk, I will discuss the development of interpretable machine learning models to test scientific hypotheses, with a specific focus on spinal motor control. Voluntary movement requires ...

In finance, data is often incomplete because the data is unavailable, inapplicable or unreported. Unfortunately, many classical data analysis techniques — for instance, linear regression — cannot ...

A new paper in Biology Methods and Protocols, published by Oxford University Press, indicates that a new computational method may help researchers ...