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

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 ...

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 ...

Representation of a Latent Dynamics Network. The neural network consists of two sub-networks: the first (represented at the top of the figure) has the function of predicting the evolution of "latent ...

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 ...