Unverified50% confidenceFactExact time
The Nearest Neighbor Gaussian Process (NNGP) conditions the distribution of each location only on its m nearest neighbors (where m is much smaller than n), thereby sparsifying the dense covariance matrix.
1
Sources
50%
Confidence
Long-term
Relevance
10/12/2026
First Seen
Sources
Related Entities
Related Claims
UnverifiedWhen the conjugate gradient method solves symmetric positive definite linear systems, the number of iterations required for convergence is closely related to the condition number of the matrix—the larger the condition number, the slower the convergence.65% similarUnverifiedWhen conditional dependencies exist between positions, the KL divergence between the product distribution and the true joint distribution is strictly greater than zero, and it increases as dependencies grow stronger and more positions are written simultaneously.65% similarUnverified质心追踪算法在做目标关联时需要构建N×M的距离矩阵，纯Python实现下计算开销随目标数量呈线性甚至平方级增长63% similarUnverifiedThe upper-bound network size has a ninth-power dependence of p^{-9} on the Dropout retention probability p, meaning that the lower the retention probability, the more sharply the required network size grows.62% similarUnverifiedTraditional first-order Langevin algorithms (such as projected Langevin Monte Carlo) require that the log-density of the target distribution be differentiable when handling sampling on constrained domains.62% similar
Cite This Claim
Stable URI
https://kongchang.com/claim/1002317API
curl https://kongchang.com/api/v1/knowledge/claims/1002317MCP
get_claim(id=1002317)