Mixed neural posterior estimation for simulators with discrete and continuous parameters
Neural Posterior Estimation (NPE) enables rapid parameter inference for complex simulators with intractable likelihoods. NPE trains an inference network to estimate a probability density over parameters given data, typically assumed to be \emph{continuous}. However, many scientific models involve parameter spaces that are \emph{mixed}, that is, they contain both discrete and continuous dimensions. We address this limitation by extending NPE to mixed parameter spaces through an inference network that jointly handles discrete and continuous parameters. The inference network factorizes the joint posterior into discrete and continuous components, combining an autoregressive classifier for the...
| Authors | Jan Boelts, Cornelius Schröder, Jonas Beck et al. |
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| Primary category | cs.LG |
| Published | 13 May 2026 |
| Last updated at source | 25 September 2026 |
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