{"id":5560,"date":"2025-07-24T07:04:25","date_gmt":"2025-07-24T07:04:25","guid":{"rendered":"https:\/\/mailitics.com\/index.php\/2025\/07\/24\/2507-17030\/"},"modified":"2025-07-24T07:04:25","modified_gmt":"2025-07-24T07:04:25","slug":"2507-17030","status":"publish","type":"post","link":"https:\/\/mailitics.com\/index.php\/2025\/07\/24\/2507-17030\/","title":{"rendered":"CoLT: The conditional localization test for assessing the accuracy of neural posterior estimates"},"content":{"rendered":"<p>    CoLT: The conditional localization test for assessing the accuracy of neural posterior estimates<br \/>\n \t<BR><br \/>\n<BR><\/BR><br \/>\n    <!-- no image --><br \/>\n \t<BR><br \/>\n<BR><\/BR><\/p>\n<div>arXiv:2507.17030v1 Announce Type: new<br \/>\nAbstract: We consider the problem of validating whether a neural posterior estimate ( q(theta mid x) ) is an accurate approximation to the true, unknown true posterior ( p(theta mid x) ). Existing methods for evaluating the quality of an NPE estimate are largely derived from classifier-based tests or divergence measures, but these suffer from several practical drawbacks. As an alternative, we introduce the emph{Conditional Localization Test} (CoLT), a principled method designed to detect discrepancies between ( p(theta mid x) ) and ( q(theta mid x) ) across the full range of conditioning inputs. Rather than relying on exhaustive comparisons or density estimation at every ( x ), CoLT learns a localization function that adaptively selects points $theta_l(x)$ where the neural posterior $q$ deviates most strongly from the true posterior $p$ for that $x$. This approach is particularly advantageous in typical simulation-based inference settings, where only a single draw ( theta sim p(theta mid x) ) from the true posterior is observed for each conditioning input, but where the neural posterior ( q(theta mid x) ) can be sampled an arbitrary number of times. Our theoretical results establish necessary and sufficient conditions for assessing distributional equality across all ( x ), offering both rigorous guarantees and practical scalability. Empirically, we demonstrate that CoLT not only performs better than existing methods at comparing $p$ and $q$, but also pinpoints regions of significant divergence, providing actionable insights for model refinement. These properties position CoLT as a state-of-the-art solution for validating neural posterior estimates.<\/div>\n<p> \t<BR><br \/>\n <BR><\/BR><br \/>\n    Tianyu Chen, Vansh Bansal, James G. Scott<br \/>\n \t<BR><br \/>\n<BR><\/BR><br \/>\n<a href=\"https:\/\/arxiv.org\/abs\/2507.17030\">Go to original source<\/a><br \/>\n \t<BR><br \/>\n <BR><\/BR><\/p>\n","protected":false},"excerpt":{"rendered":"<p>CoLT: The conditional localization test for assessing the accuracy of neural posterior estimates arXiv:2507.17030v1 Announce Type: new Abstract: We consider the problem of validating whether a neural posterior estimate ( q(theta mid x) ) is an accurate approximation to the true, unknown true posterior ( p(theta mid x) ). Existing methods for evaluating the quality [&hellip;]<\/p>\n","protected":false},"author":2,"featured_media":0,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[62,113,112],"tags":[3315,1863,1948],"class_list":["post-5560","post","type-post","status-publish","format-standard","hentry","category-aimldsaimlds","category-cs-lg","category-stat-ml","tag-colt","tag-posterior","tag-theta"],"_links":{"self":[{"href":"https:\/\/mailitics.com\/index.php\/wp-json\/wp\/v2\/posts\/5560"}],"collection":[{"href":"https:\/\/mailitics.com\/index.php\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/mailitics.com\/index.php\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/mailitics.com\/index.php\/wp-json\/wp\/v2\/users\/2"}],"replies":[{"embeddable":true,"href":"https:\/\/mailitics.com\/index.php\/wp-json\/wp\/v2\/comments?post=5560"}],"version-history":[{"count":0,"href":"https:\/\/mailitics.com\/index.php\/wp-json\/wp\/v2\/posts\/5560\/revisions"}],"wp:attachment":[{"href":"https:\/\/mailitics.com\/index.php\/wp-json\/wp\/v2\/media?parent=5560"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/mailitics.com\/index.php\/wp-json\/wp\/v2\/categories?post=5560"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/mailitics.com\/index.php\/wp-json\/wp\/v2\/tags?post=5560"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}