aiqbench analysis
Causal Posterior Estimation introduces a novel Bayesian inference technique for simulator models where likelihood evaluation is intractable, leveraging flow matching to approximate posterior distributions. By hard-coding conditional dependencies from the model's graphical representation into the neural network architecture, this method achieves high accuracy without relying on data-driven learning of these structures. This advancement addresses a significant computational bottleneck in complex simulation environments, offering a robust alternative for scenarios where traditional likelihood-based inference fails.
Causal Posterior Estimation
arXiv:2505.21468v2 Announce Type: replace Abstract: We present Causal Posterior Estimation (CPE), a novel method for Bayesian inference in simulator models, where evaluating the likelihood function is intractable or computationally expensive, but generating outputs given parameter values is straightforward. CPE approximates the posterior distribution using flow matching while directly incorporating the conditional dependence structure induced by the model's graphical representation into the neural network architecture. Across extensive experiments, we demonstrate that hard-coding these conditional dependencies into the network, rather than requiring them to be learned from data, enables CPE to achieve highly accurate posterior inference that matches or outperforms state-of-the-art baselines.
Abstract:We present Causal Posterior Estimation (CPE), a novel method for Bayesian inference in simulator models, where evaluating the likelihood function is intractable or computationally expensive, but generating outputs given parameter values is straightforward. CPE approximates the posterior distribution using flow matching while directly incorporating the conditional dependence structure induced by the model's graphical representation into the neural network architecture. Across extensive experiments, we demonstrate that hard-coding these conditional dependencies into the network, rather than requiring them to be learned from data, enables CPE to achieve highly accurate posterior inference that matches or outperforms state-of-the-art baselines.
Machine Learning (cs.LG); Machine Learning (stat.ML)
arXiv:2505.21468 [cs.LG]
(or arXiv:2505.21468v2 [cs.LG] for this version)
https://doi.org/10.48550/arXiv.2505.21468 Focus to learn more arXiv-issued DOI via DataCite
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Full text under the publisher's open licence · 190 words
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