{"id":10175,"date":"2026-02-02T07:02:30","date_gmt":"2026-02-02T07:02:30","guid":{"rendered":"https:\/\/mailitics.com\/index.php\/2026\/02\/02\/2601-22367\/"},"modified":"2026-02-02T07:02:30","modified_gmt":"2026-02-02T07:02:30","slug":"2601-22367","status":"publish","type":"post","link":"https:\/\/mailitics.com\/index.php\/2026\/02\/02\/2601-22367\/","title":{"rendered":"Amortized Simulation-Based Inference in Generalized Bayes via Neural Posterior Estimation"},"content":{"rendered":"

Amortized Simulation-Based Inference in Generalized Bayes via Neural Posterior Estimation
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arXiv:2601.22367v1 Announce Type: new
\nAbstract: Generalized Bayesian Inference (GBI) tempers a loss with a temperature $beta>0$ to mitigate overconfidence and improve robustness under model misspecification, but existing GBI methods typically rely on costly MCMC or SDE-based samplers and must be re-run for each new dataset and each $beta$ value. We give the first fully amortized variational approximation to the tempered posterior family $p_beta(theta mid x) propto pi(theta),p(x mid theta)^beta$ by training a single $(x,beta)$-conditioned neural posterior estimator $q_phi(theta mid x,beta)$ that enables sampling in a single forward pass, without simulator calls or inference-time MCMC. We introduce two complementary training routes: (i) synthesize off-manifold samples $(theta,x) sim pi(theta),p(x mid theta)^beta$ and (ii) reweight a fixed base dataset $pi(theta),p(x mid theta)$ using self-normalized importance sampling (SNIS). We show that the SNIS-weighted objective provides a consistent forward-KL fit to the tempered posterior with finite weight variance. Across four standard simulation-based inference (SBI) benchmarks, including the chaotic Lorenz-96 system, our $beta$-amortized estimator achieves competitive posterior approximations in standard two-sample metrics, matching non-amortized MCMC-based power-posterior samplers over a wide range of temperatures.<\/div>\n

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\n Shiyi Sun, Geoff K. Nicholls, Jeong Eun Lee
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Amortized Simulation-Based Inference in Generalized Bayes via Neural Posterior Estimation arXiv:2601.22367v1 Announce Type: new Abstract: Generalized Bayesian Inference (GBI) tempers a loss with a temperature $beta>0$ to mitigate overconfidence and improve robustness under model misspecification, but existing GBI methods typically rely on costly MCMC or SDE-based samplers and must be re-run for each new dataset […]<\/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":[460,1863,1948],"class_list":["post-10175","post","type-post","status-publish","format-standard","hentry","category-aimldsaimlds","category-cs-lg","category-stat-ml","tag-beta","tag-posterior","tag-theta"],"_links":{"self":[{"href":"https:\/\/mailitics.com\/index.php\/wp-json\/wp\/v2\/posts\/10175"}],"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=10175"}],"version-history":[{"count":0,"href":"https:\/\/mailitics.com\/index.php\/wp-json\/wp\/v2\/posts\/10175\/revisions"}],"wp:attachment":[{"href":"https:\/\/mailitics.com\/index.php\/wp-json\/wp\/v2\/media?parent=10175"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/mailitics.com\/index.php\/wp-json\/wp\/v2\/categories?post=10175"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/mailitics.com\/index.php\/wp-json\/wp\/v2\/tags?post=10175"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}