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Once the correlation channel has been modelled, the conditional entropy of every bit-plane in every frequency band (that is coded according to the QM) is determined.
To adjust for ascertainment, we modelled the conditional likelihood of all family phenotypes and mutation status of all tested family members (including the index/proband), given the disease phenotypes of all family members.
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Extensions of Poisson regression are commonly applied to overdispersed count data, focused on modelling the conditional mean of the response.
Generalized Poisson regression is commonly applied to overdispersed count data, and focused on modelling the conditional mean of the response.
In this paper, we adopt this method to model the (conditional) means E[s|X] of the share values s given the covariate vector X.
In most situations, social scientists are not equipped with all the necessary covariates to successfully model the conditional relationships between variables of interest.
Briefly, this involves modeling the conditional likelihood of the observed SNP genotypes and tumor subtypes, given the disease phenotypes.
Briefly, this involves modelling the conditional likelihood of the observed SNP genotypes and tumour subtypes, given the disease phenotypes.
Thus by modeling the conditional mean of Qμ given marker information, E Q | M ) μ, captures the LD information for mapping the QTL.
Using kernel density estimators to model the conditional distribution, Hofmann and Tresp [ 64] were able to eliminate the reliance on normality.
The aim is to model the conditional distribution of the observed response variables (y i = (y i,1,..., y i,p ) t ) given the observed covariates (z i = (z i,1,..., z i,q ) t ) of subject i, i = 1,..., n.
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