Simulation-Based Inference: A Practical Guide
We provide a practical guide for applying SBI methods, outlining a structured workflow with guidelines and diagnostic tools for every stage—from setting up simulators to validating results.
sbi reloaded: A toolkit for simulation-based inference workflows
We present sbi reloaded, a comprehensive update to the sbi Python package that provides researchers with state-of-the-art algorithms and tools for simulation-based inference workflows across scientific domains.
Dissecting origins of wiring specificity in dense cortical connectomes
We dissect the origins of wiring specificity in dense cortical connectomes, revealing how geometric and biological constraints shape synaptic connectivity patterns.
Simulation-based inference for efficient identification of generative models in connectomics
We demonstrate how simulation-based inference can efficiently identify synaptic connectivity rules in dense cortical connectomes, enabling analysis of complex brain circuit organization.
Flexible and efficient simulation-based inference for models of decision-making
We propose a new method to perform simulation-based inference for mixed data e.g., with continuous and discrete data types, like they often occur in models of decision-making.
GATSBI: Generative Adversarial Training for Simulation-Based Inference
We introduce GATSBI, a method that combines generative adversarial networks with simulation-based inference to achieve better sample efficiency and accuracy in posterior estimation.
Benchmarking Simulation-Based Inference
We present a benchmark suite for simulation-based inference, systematically evaluating different methods across tasks with varying dimensionality, simulation budgets, and amortization requirements.
sbi: A toolkit for simulation-based inference
We introduce sbi, a Python package providing a unified interface for simulation-based inference methods, making these powerful techniques accessible to researchers across disciplines.