A Monte Carlo EM algorithm for generalized linear mixed - download pdf or read online

By Chen J., Zhang D.

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Furthermore, the problem size n itself is a variable that changes with each problem instance. As a result, no parallel computer, regardless of how many processors it has available, can cope with a growing problem size, as long as the number of processors is finite and fixed. This holds even if © 2008 by Taylor & Francis Group, LLC 1-20 Handbook of Parallel Computing: Models, Algorithms and Applications the finite computer is endowed with an unlimited memory and is allowed to compute for an indefinite period of time.

Computational fluid dynamics (the study of the structural and dynamic properties of moving objects, including the calculation of the velocity and pressure at various points) [11]. Suppose that we are given an algorithm for solving a certain computational problem. The algorithm consists of a number of stages, where each stage may represent, for example, the evaluation of a particular arithmetic expression such as c ← a + b. Further, let us assume that a computational stage executed at time t requires a number C(t ) of constanttime operations.

10, No. 12, December 2002, pp. 170–173. [24] A. Koves, personal communication, 2005. [25] R. Lewin, Complexity, The University of Chicago Press, Chicago, 1999. R. H. Papadimitriou, Elements of the Theory of Computation, Prentice Hall, Englewood Cliffs, NJ, 1981. [27] S. Lloyd, Programming the Universe, Knopf, New York, 2006. [28] M. Lockwood, The Labyrinth of Time: Introducing the Universe, Oxford University Press, Oxford, England, 2005. [29] H. Meijer and D. Rappaport, Simultaneous Edge Flips for Convex Subdivisions, 16th Canadian Conference on Computational Geometry, Montreal, August 2004, pp.

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A Monte Carlo EM algorithm for generalized linear mixed models with flexible random effects distribu by Chen J., Zhang D.


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