ARC Code TI: Block-GP: Scalable Gaussian Process Regression
Data and Resources
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TAR Compressed FileTAR
BlockGP.tar.gz
| Field | Value |
|---|---|
| accessLevel | public |
| accrualPeriodicity | irregular |
| bureauCode | {026:00} |
| catalog_@context | https://project-open-data.cio.gov/v1.1/schema/catalog.jsonld |
| catalog_@id | https://data.nasa.gov/data.json |
| catalog_conformsTo | https://project-open-data.cio.gov/v1.1/schema |
| catalog_describedBy | https://project-open-data.cio.gov/v1.1/schema/catalog.json |
| identifier | OCIO-Fitara-113 |
| issued | 2015-07-21 |
| landingPage | http://ti.arc.nasa.gov/opensource/projects/block-gp/ |
| modified | 2020-01-29 |
| programCode | {026:046} |
| publisher | Ames Research Center |
| resource-type | Dataset |
| source_datajson_identifier | true |
| source_hash | c05efb86cce1a1ff17ab8bd310fce4f3f16969a6 |
| source_schema_version | 1.1 |
| theme | {Management/Operations} |
| Groups |
|
| Tags |
|
| isopen | False |
| license_id | notspecified |
| license_title | License not specified |
| maintainer | Dennis Koga |
| maintainer_email | dennis.koga@nasa.gov |
| metadata_created | 2025-11-21T08:32:26.253999 |
| metadata_modified | 2025-11-21T08:32:26.254003 |
| notes | Block GP is a Gaussian Process regression framework for multimodal data, that can be an order of magnitude more scalable than existing state-of-the-art nonlinear regression algorithms. The framework builds local Gaussian Processes on semantically meaningful partitions of the data and provides higher prediction accuracy than a single global model with very high confidence. |
| num_resources | 1 |
| num_tags | 16 |
| title | ARC Code TI: Block-GP: Scalable Gaussian Process Regression |