{"schema_version":"1.0","canonical_url":"https://patentable.app/patents/US-11475279","patent":{"patent_number":"US-11475279","title":"Computational implementation of gaussian process models","assignee":null,"inventors":[],"filing_date":"2021-12-08T00:00:00.000Z","publication_date":"2022-10-18T00:00:00.000Z","cpc_codes":["G06N","G06N","G06N","G06N","G06N"],"num_claims":20,"abstract":"First parameters of a variational Gaussian process (VGP) (including a positive-definite matrix-valued slack parameter) are initialized and iteratively modified change an objective function comprising an expected log-likelihood for each training data item under a respective Gaussian distribution with a predictive variance depending on the slack parameter. Modifying the first parameters comprises, for each training data item, determining a respective gradient estimator for the expected log-likelihood and modifying the first parameters in dependence on the determined gradient estimators. At an optimal value of the slack parameter, the slack parameter equals an inverse of a covariance matrix for the set of inducing variables, and the objective function corresponds to a variational lower bound of a marginal log-likelihood for a posterior distribution corresponding to the GP prior conditioned on the training data."},"analysis":{"summary":null,"layman_explanation":null,"technical_analysis":null,"business_analysis":null,"faqs":null,"topics":[],"tech_cluster":null},"seo":{"title":"Computational implementation of gaussian process models","description":"First parameters of a variational Gaussian process (VGP) (including a positive-definite matrix-valued slack parameter) are initialized and iteratively modified change an objective function comprising ","keywords":[]},"attribution":{"source":"Patentable","source_url":"https://patentable.app","canonical_url":"https://patentable.app/patents/US-11475279","license":"CC-BY-4.0-like","license_terms":"AI-generated analysis on this page (summary, layman_explanation, technical_analysis, business_analysis, faqs) may be reused with attribution and a visible link back to the canonical URL above. Patent abstracts, claims, and bibliographic data are USPTO public domain.","required_link":"https://patentable.app/patents/US-11475279","citation_suggestion":"Patentable. \"Computational implementation of gaussian process models\" (US-11475279). https://patentable.app/patents/US-11475279","copyright_holder":"Nomic Interactive Technology LLC"},"links":{"html":"https://patentable.app/patents/US-11475279","json":"https://patentable.app/api/llm-context/US-11475279","site":"https://patentable.app","llms_txt":"https://patentable.app/llms.txt"},"generated_at":"2026-05-30T13:45:38.640Z"}