The results are compared to previous thermodynamic measurements (44, 45), to predictions using various computational models, and to expectations from the crystal structure [Protein Data Bank (PDB) entry 437D 55)].
The main purpose of this work is computational simulation of the expectations, standard deviations, skewness and kurtosis of the homogenized tensor for some composites with metallic components.
In particular, we address the design of systems that manage their computational resources by using expectations about the performance of decision-making procedures and preferences over the outcomes resulting from applying those procedures.
The approach used here is to perform computational experiments, without prior expectations regarding the patterns of mouse or fruit fly data.
Despite their importance, experimental structural data are sparse, resulting in high expectations for computational modelling tools to help fill this gap.
And fundamentals of the programs used become clearer to the student as they can relate the computational findings to their expectations based on what they already know about their own projects.
The flood of high-throughput biological data has led to the expectation that computational (or in silico) models can be used to direct biological discovery, enabling biologists to reconcile heterogeneous data types, find inconsistencies and systematically generate hypotheses.
We used simulation studies to investigate the computational properties of the expectation-maximization algorithm and compare the LFLMM model with other approaches for high-dimensional longitudinal data analysis.
Haplotype frequencies were estimated using the composite haplotype method [28], which requires less computational time than the expectation-maximization algorithm.
System Reduction and Solution Algorithms for Singular Linear Difference Systems under Rational Expectations (with Robert G. King), Computational Economics, (October 2002), 20, pp. 57-86.
