1. muldo(p1,p2,[x,∂x]|lim=n) または muldo(p1,p2,x|lim=n) muldo(p1,p2,[[x1,∂x1],[x2,∂x2],. . .]|lim=n) :: 有理函数(初等函数でもよい)係数の常(または偏)微分作用素(の行列)の積 (⇐ [∂x, x] = 1) 2. muledo(p1,p2,[x,∂x]) または muledo(p1,p2,x) :: Euler型常微分作用素(の行列)の積 (⇐ [∂x, x] = x) 3. transpdo(p,[[x1,∂x1],[x2,∂x2],. . .],[[y1,∂y1],[y2,∂y2],. . .]|ex=1) :: 微分作用素の変換(xi 7→ yi = yi(x), ∂xj 7→ ∂yj = cj(x) + ∑ ν ajν(x) ∂xν) 4. translpdo(p,[[x1,...

Latent class analysis (LCA) has been used to model measurement error, to identify flawed survey questions, and to compare mode effects. Using data from NSFG Cycle 6, we examined the measurement error properties of responses to questions asked in two different modes (CAPI and ACASI). To evaluate mode effects within LCA strong assumptions need to be made about prevalence and response error rates ...

The Liechtenstein Institute on Self-Determination at Princeton University (LISD) convened a special seminar, “Grand Strategy and Self-Determination,” at Princeton University’s Woodrow Wilson School of Public and International Affairs on November 19-20, 2014. This Liechtenstein Colloquium (LCM) commemorated the twenty-year anniversary of the Liechtenstein Initiative on the study of self-determin...

This paper reports a construction of typologies of players based on their strategic reasoning in turn-taking games. Classifications have been done based on latent class analysis and according to different orders of theory of mind, and exploratory validations have been provided for the resulting classifications. Finally, interaction of the typologies described by these classifications is discuss...

The unique goal of the Living Computer Museum (LCM) in Seattle, Washington, is to keep old computer technology powered up and ready to be used by patrons. Instead of sheltering its rare arti facts in climate-controlled glass cases, most of the computers at the LCM are turned on and accompanied by a user manual to read, a chair to sit in, and a keyboard to type on so you can explore what it was ...

Environmental data is nearly always multivariate and often spatial–temporal. Thus to interpolate the data in space or to predict in space–time it is necessary to use a multivariate spatial–temporal method. Cokriging is easily extended to spatial–temporal data if there are valid space–time variograms or covariance functions. Various authors have proposed such models. In this paper, a generalized...