Probabilistic Latent Variable Modeling Toolbox for Multisubject Data
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PLVM - Probabilistic Latent Variable Modeling toolbox for multisubject data

The Probabilistic Latent Variable Modeling Toolbox for Multisubject Data holds a collection of latent variable algorithms implemented in Matlab™. The algorithms support the use of graphical processing units (GPUs) for high performance computing. All code can be used freely in research and other non-profit applications. If you publish results obtained with this toolbox we kindly ask that our and other relevant sources are properly cited.

This toolbox has been developed at:

The Technical University of Denmark, Department for Applied Mathematics and Computer Science, Section for Cognitive Systems.

The toolbox was developed in connection with the Brain Connectivity project at DTU ( .


  • psFA
    • Probabilistic Sparse Factor Analysis (psFA). Models subject specific heteroscedastic feature/voxel noise.
  • psPCA
    • Probabilistic Sparse Principal Component Analysis (psPCA). Models subject specific homoscedastic noise.

Common algorithm properties

  • No parameters need to be set by default.
  • All models support modeling multiple subjects.
  • An approximate model solution is found using variational inference.
  • The evidence lowerbound is calculated (an approximation to log likelihood).
  • Estimating number of components using Automatic Relevance Determination (ARD).
  • Ability to individually turn off modeling aspects such as sparsity, ARD and noise modeling.


  • demo_psFA
    • Demostration on a toy example showing model settings and their effect
  • calcICASSP17
    • Synthetic experiments in the original psFA article (which is currently under review).