MBB-team/VBA-toolbox

Variational Bayesian Analysis (VBA)
1.4K Downloads
Updated 11 Apr 2023

VBA is a fully Bayesian toolbox for model-based data analyses.
Most computational models 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)
Note: The VBA toolbox is a collaborative project. Please contact us if you want to contribute. You can also fork the repo from Github and ask us to pull your contribution.

Cite As

Jean Daunizeau (2024). MBB-team/VBA-toolbox (https://github.com/MBB-team/VBA-toolbox), GitHub. Retrieved .

MATLAB Release Compatibility
Created with R2011a
Compatible with any release
Platform Compatibility
Windows macOS Linux
Categories
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core

core/diagnostics

core/display

core/sugar

core/unormalizedLikelihood

demos/0_basics

demos/1_advanced

demos/2_statistics

demos/3_behavioural

demos/4_neural

demos/5_classification

demos/6_physics

demos/7_mathematics

demos/_models

legacy

legacy/trashbin

modules/DCM

modules/GLM

modules/OTO

modules/classical_statistics

modules/classification

modules/classification/BMM

modules/classification/CRP

modules/classification/GMM

modules/random_field_theory

modules/theory_of_mind

sandbox

tests

tests/demos

tests/utils

thrid-party

thrid-party/spm

utils

Versions that use the GitHub default branch cannot be downloaded

Version Published Release Notes
1.0.0.0

...

Includes additional features, e.g.:
- random-effect model selection
- empirical Bayes procedures
- clustering analyses (mixture of gaussian and/or binomial distributions)
- additions to the library of models

To view or report issues in this GitHub add-on, visit the GitHub Repository.
To view or report issues in this GitHub add-on, visit the GitHub Repository.