Further remark on stably updating mean and standard deviation estimates
Communications of the ACM
Updating mean and variance estimates: an improved method
Communications of the ACM
Stably updating mean and standard deviation of data
Communications of the ACM
Remark on stably updating mean and standard deviation of data
Communications of the ACM
Rounding Errors in Algebraic Processes
Rounding Errors in Algebraic Processes
Computational efficiency evaluation in output analysis
Proceedings of the 29th conference on Winter simulation
Precision averaging for real-time analysis
Communications of the ACM
Updating mean and variance estimates: an improved method
Communications of the ACM
A simulation course for computer science students
SIGCSE '80 Proceedings of the eleventh SIGCSE technical symposium on Computer science education
Statistics for computer scientists
ACM SIGCSE Bulletin
The singular value decomposition in multivariate statistics
ACM SIGNUM Newsletter
Development of a simulation model of colorectal cancer
ACM Transactions on Modeling and Computer Simulation (TOMACS)
Handling numeric attributes in hoeffding trees
PAKDD'08 Proceedings of the 12th Pacific-Asia conference on Advances in knowledge discovery and data mining
A new approach for cluster detection for large datasets with high dimensionality
DaWaK'05 Proceedings of the 7th international conference on Data Warehousing and Knowledge Discovery
Multi-resolution surfel maps for efficient dense 3D modeling and tracking
Journal of Visual Communication and Image Representation
| Hi-index | 48.25 |
Four algorithms for the numerical computation of the standard deviation of (unweighted) sampled data are analyzed. Two of the algorithms are well-known in the statistical and computational literature; the other two are new algorithms specifically intended for automatic computation. Our discussion is expository, with emphasis on reaching a suitable definition of “accuracy.” Each of the four algorithms is analyzed for the conditions under which it will be accurate. We conclude that all four algorithms will provide accurate answers for many problems, but two of the algorithms, one new, one old, are substantially more accurate on difficult problems than are the other two.