Dynamic Inspection of Latent Variables in State-Space Systems

State Space Techniques in Structural Equation Modeling Transformation of latent variables in and out of latent variable models

Neural modeling of dynamic systems with nonmeasurable state variables

The paper studies the ability possessed by recurrent neural networks to model dynamic systems when some relevant state variables are not measurable. Neural architectures based on virtual states—which naturally arise from a space state representation—are introduced and compared with the more traditional neural output error ones. Despite the evident potential model ability possessed by virtual st...

Latent Variables, State Spaces, and Mixing

We argue that many models for multivariate longitudinal and cross-sectional data analysis have a common ancestry. They all are based on the qualitative idea that if we knew the actual state of the world, the relations between the observed quantities would be truly simple. This is shown to lead directly to factor analysis, IRT, state space models, mixture densities, latent Markov chains, MIMIC, ...

Evolving Latent Space Model for Dynamic Networks

Networks observed in real world like social networks, collaboration networks etc., exhibit temporal dynamics, i.e. nodes and edges appear and/or disappear over time. In this paper, we propose a generative, latent space based, statistical model for such networks (called dynamic networks). We consider the case where the number of nodes is fixed, but the presence of edges can vary over time. Our m...

Elimination of latent variables in behavioral systems: a geometric approach ⋆

This paper considers computational aspects of the problem of elimination of variables. More precisely, the problem of elimination of latent variables in models is addressed in the behavioral framework. In earlier contributions on this theme, the model classes of infinitely smooth C and square integrable L2 linear time-invariant systems have been considered. For both system classes, conditions f...