for the insertion site statistic of the gene essentiality analysis data

rancisella Tularensis species. When producing this plot, genes

sified by a prediction model [Yang, et al., 2017].

he histogram generated using ggplot2 for the gene essentiality analysis data

cisella Tularensis species. The blue bars correspond to the predicted essential

he red bars correspond to the predicted non-essential genes.

e parametric approach

ametric approach estimates a density function based on the

on that a data set follows a pre-specified single parametric

on such as a Gaussian distribution or a Gamma distribution

an, 1986; Duda, et al., 2000]. Based on this assumption, the

arameters are estimated using either the likelihood maximisation

or the Bayesian learning method. For instance, if N data points

ݔଶ, ⋯, ݔேሻ are expected to follow a Gaussian distribution, the

d maximisation approach can estimate the model parameters (or

butional statistics) using the following equations,