BayesFlow: Learning Complex Stochastic Models With Invertible Neural Networks.

Area of Science:

• Computational science
• Statistical modeling
• Machine learning

Background:

• Parameter estimation is crucial across scientific disciplines.
• Complex models often lack explicit likelihood functions, hindering traditional inference.
• Existing methods struggle with intricate models and handcrafted summary statistics.

Purpose of the Study:

• To introduce BayesFlow, a novel method for globally amortized Bayesian inference.
• To develop a simulation-based approach for learning probabilistic mappings from data to model parameters.
• To create a general framework applicable to diverse intractable modeling scenarios.

Main Methods:

• Utilizes invertible neural networks for Bayesian inference.
• Employs simulations to train a global estimator for parameter inference.
• Incorporates a summary network to learn maximally informative data embeddings.
• Achieves amortized inference without additional training or optimization for new datasets.

Main Results:

• Demonstrates successful application of BayesFlow on intractable models in population dynamics, epidemiology, cognitive science, and ecology.
• Shows that pretrained neural networks can infer full posteriors on new datasets.
• Highlights the method's ability to learn effective summary statistics from data.

Conclusions:

• BayesFlow offers a powerful and generalizable framework for amortized Bayesian parameter estimation.
• The method overcomes limitations of traditional techniques for complex, likelihood-free models.
• It facilitates efficient inference across a wide range of scientific modeling challenges.