Abstract:
Most scientific fields estimate mathematical model parameters. When processes and model descriptions become complex and there is no explicit likelihood function, this problem can be difficult. We propose BayesFlow, an invertible neural network-based globally amortized Bayesian inference method. Simulations learn a global estimator for probabilistic mapping from observed data to model parameters. This pretrained neural network can infer full posteriors on arbitrarily many real data sets involving the same model family without further training or optimization. We also use a summary network to embed observed data into maximally informative summary statistics. The method can model scenarios where handcrafted summary statistics fail. BayesFlow solves difficult population dynamics, epidemiology, cognitive science, and ecology models. BayesFlow allows amortized Bayesian parameter estimation machines for any forward model that can simulate data.
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