{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/variationally-inferred-sampling-through-a-1","title":"Variationally Inferred Sampling Through a Refined Bound","arxiv_id":null,"date":"2019-10-16","proceeding":"pproximateinference AABI Symposium 2019 12","authors":["Victor Gallego","David Rios Insua"],"abstract":"A framework for efficient Bayesian inference in probabilistic programs is introduced by embedding a sampler inside a variational posterior approximation. Its strength lies in both ease of implementation and automatically tuning sampler parameters to speed up mixing time. Several strategies to approximate the evidence lower bound (ELBO) computation are introduced, including a rewriting of the ELBO objective. Experimental evidence is shown by performing experiments on an unconditional VAE on density estimation tasks; solving an influence diagram in a high-dimensional space with a conditional variational autoencoder (cVAE) as a deep Bayes classifier; and state-space models for time-series data.","url_abs":"https://openreview.net/forum?id=Hkglty2EKH","url_pdf":"https://openreview.net/pdf?id=Hkglty2EKH","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"variationally-inferred-sampling-through-a-1","repo_url":"https://github.com/vicgalle/vis","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"pytorch","reach":null}],"tasks":[{"task_slug":"bayesian-inference","task_name":"Bayesian Inference"},{"task_slug":"density-estimation","task_name":"Density Estimation"},{"task_slug":"state-space-models","task_name":"State Space Models"},{"task_slug":"time-series-1","task_name":"Time Series"},{"task_slug":"time-series","task_name":"Time Series Analysis"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}