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Since we assume that the datasets are obtained independently, we apply the inverse chi-square method and obtain the meta chi-square statistics: (16) where P i is the p-value obtained from the i th data set for a given gene pair defined in Equation (15).
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We assume that the dataset X = {x i } consists of independently and identically distributed random variables that are governed by the distribution function F x).
We assume that the dataset generated in Linköping is more likely to be authentic than that generated in Stockholm/Uppsala, for three reasons.
We assume that the dataset consists of multiple sets (clusters).
To construct the simulation experiments we assume that the dataset is comprised of dominant pathways that define the groups and random noise pathways.
Both methods gave 4 as the most probable value of K, so we can assume that the dataset includes 4 parental populations that are distinguishable on a genetic basis.
In the rest of the paper, we always assume that: i) The dataset ( mathbf{x}={left{{mathbf{x}}_mright}}_{m=1}^M ) is composed of M independent and identically distributed (IID) N-dimensional, zero-mean, complex t-distributed random vectors.
In particular, we assumed that the datasets were complete (and thus that all infected farms were detected) and that only the first farm in each outbreak was infected from an outside source.
Some breeders compute heritability using methods assuming that the datasets are balanced and that the genotypes are independent.
Almost all existing data analysis and data mining tools such as clustering tools, inductive learning tools, and statistical analysis tools assume that datasets to be analysed are represented through a structured file format.
Assuming that the dataset is physically stored in such a file or in a relational database, a minimal set of attributes should be defined to facilitate reproduction of the analysis, as well as enable reading and loading into visualization tools with minimal user input.
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