<ul data-eligibleForWebStory="true"><li>Neural likelihood estimation methods for simulation-based inference face issues with high-dimensional data.</li><li>A new method called Surjective Sequential Neural Likelihood (SSNL) estimation is introduced for simulation-based inference.</li><li>SSNL utilizes a dimensionality-reducing surjective normalizing flow model as a surrogate likelihood function.</li><li>It enables computational inference via Markov chain Monte Carlo or variational Bayes methods.</li><li>SSNL eliminates the need for manual crafting of summary statistics for high-dimensional data inference.</li><li>The method is evaluated on various experiments and surpasses or matches state-of-the-art techniques.</li><li>SSNL proves to be a promising option for simulation-based inference on high-dimensional data sets.</li></ul>

Simulation-based Inference for High-dimensional Data using Surjective Sequential Neural Likelihood Estimation

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