Image and Mathematical Modeling

New technologies have opened the door to the era of big data and with it the emergence of data science. In a massive world of information, the need for statistical treatment of events that evolve in space-time on a network system is increasingly common. When these events are monitored over time they give information on the displacement and provide trajectories over networks. The theory of stochastic space- time processes on networks provide the theoretical and methodological support for this type of data.

This project aims to address the statistical modeling of this type of event in these new scenarios. We will begin with the parametric analysis of first-order
characteristics, which are necessary to arrive at those of second order and higher orders. We intend to characterize all the orders (as far as possible) of a specific process on networks to determine the underlying structure of space-time interaction existing on the network.

MODELOS ESTOCÁSTICOS E INFERENCIA PARA PROCESOS MARCADOS ESPACIO-TEMPORALES SOBRE REDES

UJI-B2021-37

NEURO-ML: MACHINE LEARNING THROUGH BIO-INSPIRED MODELS (AND VICE VERSA)

UJI-A2022-12