A forward-only scheme for online learning of proposaldistributions in particle filters

A forward-only scheme for online learning of proposaldistributions in particle filters | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article A forward-only scheme for online learning of proposaldistributions in particle filters Sylvain Procope-Mamert, Nicolas Chopin, Maud Delattre, Guillaume Kon Kam King This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8788280/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 7 You are reading this latest preprint version Abstract We introduce a new online approach for constructing proposal distributions in particle filters using a forward scheme. Our method progressively incorporates future observations to refine proposals. This is in contrast to backward-scheme algorithms that require access to the entire dataset, such as the iterated auxiliary particle filters \citep{guarniero_iterated_2017} and controlled sequential Monte Carlo \citep{heng_controlled_2020}, which leverage all future observations through backward recursion. In comparison, our forward scheme achieves a gradual improvement of proposals that converges toward the proposal targeted by these backward methods. We show that backward approaches can be numerically unstable even in simple settings. Our forward method, however, offers significantly greater robustness with only a minor trade-off in performance, measured by the variance of the marginal likelihood estimator. Numerical experiments on both simulated and real data illustrate the enhanced stability of our forward approach. Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Reviews received at journal 13 May, 2026 Reviewers agreed at journal 12 Feb, 2026 Reviewers agreed at journal 12 Feb, 2026 Reviewers invited by journal 09 Feb, 2026 Editor assigned by journal 05 Feb, 2026 Submission checks completed at journal 05 Feb, 2026 First submitted to journal 04 Feb, 2026 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-8788280","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":588556002,"identity":"f4ecf6f8-0f19-409c-ad50-8c236eb8818c","order_by":0,"name":"Sylvain Procope-Mamert","email":"","orcid":"","institution":"National Research Institute for Agriculture, Food and Environment","correspondingAuthor":false,"prefix":"","firstName":"Sylvain","middleName":"","lastName":"Procope-Mamert","suffix":""},{"id":588556003,"identity":"f510aba8-c6bc-45d5-a756-47b18c93d4fc","order_by":1,"name":"Nicolas Chopin","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABDElEQVRIie3RwUrDMBjA8a8Usku115z0FSKCIsh8EC9fGMzLcvIqpTDIrZ4HA/cK87ZjJJBTcVdhIukbeBJ6ETO2OIQGPQrLH1Jamh/9QgFisf9Y6pZd31C3EC7dNfOvVJjgjgwBiCcEw1/6JgD6d3LeSxuLdwXk07GxzWJ5nc8qBe3i7QrIge0iF2NyytBooK/mhvF6JSbmEJOqvuUl6bEuwnRGKBIFjI7OKJcrUZqMpYlEhGPZOdiGfBaePIvZlrjBSJhwmXqixHxLkjJI3Fn4vc7oy9CdRQ7Eoxmxp0oilyGy1I19/yiO8snANK3siwddn9jWDZYHiG/9L3Y7FPx4DPeXPbFYLLaPfQGlrVhXKY4lSQAAAABJRU5ErkJggg==","orcid":"","institution":"École Nationale de la Statistique et de l'Administration Économique","correspondingAuthor":true,"prefix":"","firstName":"Nicolas","middleName":"","lastName":"Chopin","suffix":""},{"id":588556004,"identity":"0fb9da6a-4012-45de-98a1-8b91ee6be369","order_by":2,"name":"Maud Delattre","email":"","orcid":"","institution":"National Research Institute for Agriculture, Food and Environment","correspondingAuthor":false,"prefix":"","firstName":"Maud","middleName":"","lastName":"Delattre","suffix":""},{"id":588556005,"identity":"98afd429-b394-422d-8d70-a172f0bff960","order_by":3,"name":"Guillaume Kon Kam King","email":"","orcid":"","institution":"National Research Institute for Agriculture, Food and Environment","correspondingAuthor":false,"prefix":"","firstName":"Guillaume","middleName":"Kon Kam","lastName":"King","suffix":""}],"badges":[],"createdAt":"2026-02-04 15:32:08","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8788280/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8788280/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":102746219,"identity":"d335930d-ae97-49fa-a76a-7c848b7ca944","added_by":"auto","created_at":"2026-02-16 08:56:08","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":794341,"visible":true,"origin":"","legend":"","description":"","filename":"forwardarticlesylvain.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8788280/v1_covered_74402463-4282-4dae-b258-1dd2524fb2b7.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"A forward-only scheme for online learning of proposaldistributions in particle filters","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":true,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":true,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"statistics-and-computing","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"stco","sideBox":"Learn more about [Statistics and Computing](http://link.springer.com/journal/11222)","snPcode":"11222","submissionUrl":"https://submission.nature.com/new-submission/11222/3","title":"Statistics and Computing","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"","lastPublishedDoi":"10.21203/rs.3.rs-8788280/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8788280/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e We introduce a new online approach for constructing proposal distributions in particle filters using a forward scheme. 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