{"paper":{"title":"Towards automatic calibration of the number of state particles within the SMC$^2$ algorithm","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"stat.CO","authors_text":"James Ridgway, Mathieu Gerber, Nicolas Chopin, Omiros Papaspiliopoulos","submitted_at":"2015-06-01T17:01:56Z","abstract_excerpt":"SMC$^2$ is an efficient algorithm for sequential estimation and state inference of state-space models. It generates $N_{\\theta}$ parameter particles $\\theta^{m}$, and, for each $\\theta^{m}$, it runs a particle filter of size $N_{x}$ (i.e. at each time step, $N_{x}$ particles are generated in the state space $\\mathcal{X}$). We discuss how to automatically calibrate $N_{x}$ in the course of the algorithm. Our approach relies on conditional Sequential Monte Carlo updates, monitoring the state of the pseudo random number generator and on an estimator of the variance of the unbiased estimate of the"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1506.00570","kind":"arxiv","version":1},"verdict":{"id":null,"model_set":{},"created_at":null,"strongest_claim":"","one_line_summary":"","pipeline_version":null,"weakest_assumption":"","pith_extraction_headline":""},"references":{"count":0,"sample":[],"resolved_work":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","internal_anchors":0},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"author_claims":{"count":0,"strong_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"builder_version":"pith-number-builder-2026-05-17-v1"}