ForwardFlow: Simulation only statistical inference using deep learning
Deep learning models are being used for the analysis of parametric statistical models based on simulation-only frameworks. Bayesian models using normalizing flows simulate data from a prior distribution and are composed of two deep neural networks: a summary network that learns a sufficient statistic for the parameter and a normalizing flow that conditional on the summary network can approximate the posterior distribution. Here, we explore frequentist models that are based on a single summary network. During training, input of the network is a simulated data set based on a parameter and the loss function minimizes the mean-square error between learned summary and parameter. The network th...
| Authors | Stefan Böhringer |
|---|---|
| Primary category | math.ST |
| Published | 11 March 2026 |
| Last updated at source | 11 March 2026 |
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