Inference Methods & Examples

VBI provides multiple approaches for parameter inference, each with different strengths and use cases.

Inference Method Overview

Inference Methods Comparison

Method

Dependencies

Performance

Best For

CDE (Conditional Density Estimation)

NumPy + autograd

Fast, lightweight

Quick inference, limited resources

SBI (Simulation-Based Inference)

PyTorch + sbi

State-of-the-art

Complex problems, research

CDE-Based Inference (Lightweight)

Conditional Density Estimation using pure NumPy implementation - no PyTorch required.

Key Features:

  • ✅ Lightweight: Only requires NumPy and autograd

  • ✅ Fast: Efficient implementation for moderate-scale problems

  • ✅ Transparent: Clear mathematical foundation

  • ✅ Flexible: Easy to customize and extend

Available Methods:

When to use CDE:

  • Limited computational resources

  • Want to avoid heavy PyTorch dependency

  • Need transparent, interpretable inference

SBI-Based Inference (Advanced)

Simulation-Based Inference using PyTorch and the sbi library - state-of-the-art methods.

Key Features:

  • 🚀 State-of-the-art: Latest neural density estimation techniques

  • 🧠 Scalable: Handles high-dimensional problems efficiently

  • 🔧 Comprehensive: Multiple inference algorithms (SNPE, SNLE, SNRE)

Current Examples:

Note: Most simulation examples currently use SBI for inference. We’re working on separating these into dedicated inference tutorials.

When to use SBI:

  • Working with high-dimensional parameter spaces (> 10 parameters)

  • Need state-of-the-art performance

  • Complex, multimodal posterior distributions

Getting Started

For CDE Inference:

  1. Start with CDE Inference with MDN (Mixture Density Networks) - Learn MDN basics

  2. Try CDE Inference with MAF (Masked Autoregressive Flows) - More advanced MAF method

  3. Apply to your brain model of choice

For SBI Inference:

  1. Install full VBI: pip install vbi[inference]

  2. Check existing simulation examples that include SBI

  3. Adapt to your specific use case

Upcoming Examples

We’re working on expanding the inference examples:

  • CDE + Brain Models: Direct application to Jansen-Rit, Wilson-Cowan models

  • SBI Tutorials: Dedicated SBI workflow examples

  • Method Comparisons: Side-by-side CDE vs SBI comparisons

  • Real Data Examples: Using experimental neuroimaging data

Performance Guidelines

CDE Performance Tips:

  • Use appropriate number of mixture components (MDN) or layers (MAF)

  • Monitor training convergence carefully

  • Consider data preprocessing/normalization

SBI Performance Tips:

  • Use GPU acceleration when available

  • Tune simulation budget vs accuracy trade-off

  • Consider sequential vs single-round inference

Memory and Speed:

  • CDE: Lower memory usage, faster setup

  • SBI: Higher memory usage, potentially faster inference for complex problems

Contributing

We welcome contributions of new inference examples! See our CONTRIBUTING.md for guidelines on adding new tutorials and examples.