VBA

This page describes a variational Bayesian toolbox for the quantitative analysis of neurobiological and behavioural data.

Most models of neurobiological and behavioural data can be broken down into processes that evolve over time and static observation mappings. Given these evolution and observation mappings, the toolbox can be used to simulate data, perform statistical data analysis, optimize the experimental design, etc... In short, this toolbox relies upon a probabilistic approach to model-based analysis of multivariate time series. It provides:

• • • plug-and-play tools for classical statistical tests

    • a library of computational models of behavioural and neurobiological data time series

    • quick and efficient probabilistic inference techniques for parameter estimation and model comparison (+ experimental design optimization)

    • graphical visualization of results (+ advanced diagnostics of model inversion)

Want to get started? Please visit the VBA wiki pages...

When using VBA, please cite the following papers:

Variational Bayesian identification and prediction of stochastic nonlinear dynamic causal models

J. Daunizeau, K.J. Friston, S.J. Kiebel

Physica D: nonlinear phenomena (2009), 238: 2089-2118 [PubMed]

VBA: a probabilistic treatment of nonlinear models for neurobiological and behavioural data

J. Daunizeau, V. Adam, L. Rigoux

PLoS Comput Biol (2014), 10(1): e1003441 [Pubmed]