Automatic building of radiographic flexible models using a set of examples

Author(s):  
S. Girard
Keyword(s):  
Author(s):  
Dexter Cahoy ◽  
Elvira Di Nardo ◽  
Federico Polito

AbstractWithin the framework of probability models for overdispersed count data, we propose the generalized fractional Poisson distribution (gfPd), which is a natural generalization of the fractional Poisson distribution (fPd), and the standard Poisson distribution. We derive some properties of gfPd and more specifically we study moments, limiting behavior and other features of fPd. The skewness suggests that fPd can be left-skewed, right-skewed or symmetric; this makes the model flexible and appealing in practice. We apply the model to real big count data and estimate the model parameters using maximum likelihood. Then, we turn to the very general class of weighted Poisson distributions (WPD’s) to allow both overdispersion and underdispersion. Similarly to Kemp’s generalized hypergeometric probability distribution, which is based on hypergeometric functions, we analyze a class of WPD’s related to a generalization of Mittag–Leffler functions. The proposed class of distributions includes the well-known COM-Poisson and the hyper-Poisson models. We characterize conditions on the parameters allowing for overdispersion and underdispersion, and analyze two special cases of interest which have not yet appeared in the literature.


2014 ◽  
Vol 621 ◽  
pp. 253-259
Author(s):  
Jing Qian ◽  
Ling Wei Meng

Based on the automatic dynamic analysis of mechanical systems software, both rigid and flexible models of the space-swing mechanism for the superpave gyratory compactor are developed. The structural analysis shows that the length and the initial phase of cranks, and the assembling accuracy (coordinates) of some points are very sensitive relative to the waving of compaction angle. Greater rigidity helps stabilize the change of the compaction angles.


Author(s):  
Christopher H. Schmid ◽  
Kerrie Mengersen

This chapter introduces a Bayesian approach to meta-analysis. It discusses the ways in which a Bayesian approach differs from the method of moments and maximum likelihood methods described in chapters 9 and 10, and summarizes the steps required for a Bayesian analysis. It shows that Bayesian methods provide the basis for a rich variety of very flexible models, explicit statements about uncertainty of model parameters, inclusion of other information relevant to an analysis, and direct probabilistic statements about parameters of interest. In a meta-analysis context, this allows for more straightforward accommodation of study-specific differences and similarities, nonnormality and other distributional features of the data, missing data, small studies, and so forth.


2011 ◽  
Vol 80 (5) ◽  
pp. 1088-1096 ◽  
Author(s):  
Charles B. Yackulic ◽  
Stephen Blake ◽  
Sharon Deem ◽  
Michael Kock ◽  
María Uriarte

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