12 citations · 26 across the 5 of their papers we have counts for
5 papers
The Gaussian-Linear Hidden Markov model: a Python package
Diego Vidaurre, Laura Masaracchia, Nick Y. Larsen +4
We propose the Gaussian-Linear Hidden Markov model (GLHMM), a generalisation of different types of HMMs commonly used in neuroscience. In short, the GLHMM is a general framework wh…
Dynamic functional connectivity: why the controversy?
Diego Vidaurre
In principle, dynamic functional connectivity in fMRI is just a statistical measure. A passer-by might think it to be a specialist topic, but it continues to attract widespread att…
Breakdown of random matrix universality in Markov models
Faheem Mosam, Diego Vidaurre, Eric De Giuli
Biological systems need to react to stimuli over a broad spectrum of timescales. If and how this ability can emerge without external fine-tuning is a puzzle. We consider here this…
Variational Mixture Models with Gamma or inverse-Gamma components
A. Llera, D. Vidaurre, R. H. R. Pruim +1
Mixture models with Gamma and or inverse-Gamma distributed mixture components are useful for medical image tissue segmentation or as post-hoc models for regression coefficients obt…
Dimensionality reduction for time series data
Diego Vidaurre, Iead Rezek, Samuel L. Harrison +2
Despite the fact that they do not consider the temporal nature of data, classic dimensionality reduction techniques, such as PCA, are widely applied to time series data. In this pa…