# WIKI

# How to use VBA

## Some statistical theory

- Bayesian inference: introduction
- The variational Bayes approach
- VBA: structure of the generative model

## Getting Started with VBA

## What VBA users cannot ignore

- VBA: results and diagnostics
- VBA: controlling the inversion options
- 10 simple rules for VBA
- Identifiability and confusion analyses

## Advanced VBA tricks

- Experimental design optimization
- Multisession inversions
- Mixed observations (including multinomial and gaussian variables)
- Bayesian model selection for group studies
- Bayesian model selection with large model spaces
- Volterra decompositions
- Bayesian Model Averaging (BMA)
- Setting the priors through Empirical Bayes
- Setting “hard constraints” through parameter transformations
- A few useful stand-alone VBA functions
- Extending VBA’s generative model (for power-users only)

# Library of models

The VBA-toolbox already includes a large library of plug-and-play models. Below, we give a few examples of such models. We briefly expose the main theoretical and experimental issues, and point to the relevant VBA functions (demonstration scripts, evolution/observation functions, etc…). Note that this list is by no means exhaustive: users are invited to look for demonstration scripts in VBA’s

`\subfunctions`

folder.

### Behavioural/cognitive models

- Reinforcement learning
- Bayesian associative learning (including, e.g., volatile systems)
- Bayesian sequence learning (BSL)
- Bayesian mentalizing (k-ToM)
- Bi-dimensional decisions (e.g., inter-temporal choices)

### Neurobiological models

- Dynamic Causal Modeling
- Behavioural Dynamic Causal Modeling
- Neural Fields
- Spiking Neurons
- Calcium imaging using biophysical models