Unveiling the Persistent Dynamics of Visual-Motor Skill via Drifting Markov Modeling.

This study investigates the climbing dynamics of learning on a long-time scale, by using Drifting Markov models. Climbing constitutes a complex decision-making task that requires effective visual-motor coordination and exploration of the environment. Drifting Markov models, is a class of constrained heterogeneous Markov processes that allow the modeling of data that exhibit heterogeneity.

Statistical inference for a general class of distributions with time-varying parameters.

In this article we are interested in a general class of distributions for independent not necessarily identically distributed random variables, closed under minima, that includes a number of discrete and continuous distributions like the Geometric, Exponential, Weibull or Pareto. The main parameter involved in this class of distributions is assumed to be time varying with several possible modeling options. This is of particular interest in reliability and survival analysis for describing the time to event or failure.