Structure learning for Bayesian networks using the CCDr algorithm.
-
Updated
Jan 10, 2024 - C++
Structure learning for Bayesian networks using the CCDr algorithm.
BEER determines an ECC code's parity-check matrix based on the uncorrectable errors it can cause. BEER targets Hamming codes that are used for DRAM on-die ECC but can be extended to apply to other linear block codes (e.g., BCH, Reed-Solomon). BEER is described in the 2020 MICRO paper by Patel et al.: https://arxiv.org/abs/2009.07985.
Add a description, image, and links to the experimental-data topic page so that developers can more easily learn about it.
To associate your repository with the experimental-data topic, visit your repo's landing page and select "manage topics."