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Displaying records 171 to 180 of 225 records.
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171. Rectangular Distribution Whose End-points Are Not Exactly Known: Curvilinear Trapezoidal Distribution
Topic: Math
Published: 3/11/2010
Authors: Raghu N Kacker, James F Lawrence
Abstract: The state of knowledge concerning a quantity about which scant specific information is available is often represented by a rectangular probability distribution on some interval (Z1, Z2) specified by scientific judgment. Often, the end-points Z1 and ...
http://www.nist.gov/manuscript-publication-search.cfm?pub_id=903235

172. Rectangular Distribution Whose Width is Not Exactly Known: Isocurvilinear Trapezoidal Distribution
Topic: Math
Published: 6/1/2009
Authors: Raghu N Kacker, James F Lawrence
Abstract: After the Gaussian distribution, the probability distribution most commonly used in evaluating uncertainty in measurement is the rectangular distribution. When the mid-point of a rectangular distribution is fixed but the half-width is uncertain and ...
http://www.nist.gov/manuscript-publication-search.cfm?pub_id=890099

173. Reduction Formulae for Products of Theta Functions
Series: Journal of Research (NIST JRES)
Report Number: 117.017
Topic: Math
Published: 11/6/2012
Author: Peter L. Walker
Abstract: In four cases it is already known that the product of two distinct Jacobian theta functions having the same variable z and the same nome q is a multiple of a single Jacobian theta function, with the multiple independent of z. The main purpose of t ...
http://www.nist.gov/manuscript-publication-search.cfm?pub_id=911334

174. Refining the In-Parameter-Order Strategy for Constructing Covering Arrrays
Series: Journal of Research (NIST JRES)
Topic: Math
Published: 9/1/2008
Authors: Michael Forbes, James F Lawrence, Yu Lei, Raghu N Kacker, David R Kuhn
Abstract: Covering arrays are structures for well-representing extremely large input spaces and are used to efficiently implement blackbox testing for software and hardware. This paper proposes refinements over the In-Parameter-Order strategy (for arbitrary $t ...
http://www.nist.gov/manuscript-publication-search.cfm?pub_id=152141

175. Remembering Paul Garabedian
Topic: Math
Published: 6/15/2010
Author: Geoffrey B McFadden
Abstract: Paul R. Garabedian, a leader in the field of computational science, passed away on May 13th, 2010, at his home in Manhattan at the age of 82 after a long battle with cancer. During the course of sixty years of research on the faculties at Stanford an ...
http://www.nist.gov/manuscript-publication-search.cfm?pub_id=905935

176. Response to comments on Statistical analysis of CIPM key comparisons based on the ISO Guide
Topic: Math
Published: 2/15/2005
Authors: Raghu N Kacker, Raju Vsnu Datla, Albert C Parr
http://www.nist.gov/manuscript-publication-search.cfm?pub_id=150909

177. Review of Understanding and Implementing the Finite Element Method
Topic: Math
Published: 1/1/2007
Author: William F Mitchell
Abstract: This paper reviews the book "Understanding and Implementing the Finite Element Method" by Mark S. Gockenbach.
http://www.nist.gov/manuscript-publication-search.cfm?pub_id=51115

178. STOPWATCH User's Guide Version 1.0
Series: NIST Interagency/Internal Report (NISTIR)
Report Number: 5971
Topic: Math
Published: 3/1/1997
Author: William F Mitchell
http://www.nist.gov/manuscript-publication-search.cfm?pub_id=900806

179. Separating Signal From Noise in Global Warming
Topic: Math
Published: 3/12/2003
Author: Bert W Rust
Abstract: One argument often used against global warming is that the global temperature record is too noisy to allow a clear determination of the signal. This paper presents two models for the signal which suggest that: (1) the warming is accelerating, (2) th ...
http://www.nist.gov/manuscript-publication-search.cfm?pub_id=150874

180. Shape-Preserving, Multi-Scale Fitting of Bivariate Data by Cubic L^d1^ Smoothing Splines
Topic: Math
Published: 5/10/2002
Authors: David E. Gilsinn, J E Lavery
Abstract: Bivariate cubic L1 smoothing splines are introduced. The coefficients of a cubic L1 smoothing spline are calculated by minimizing the weighted sum of the L1 norms ofsecond derivatives of the spline and the l1 norm of the residuals of the data-fitting ...
http://www.nist.gov/manuscript-publication-search.cfm?pub_id=150830



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