The criticality level of a rolling stock failure is defined by a risk factor (R) which is calculated by multiplying the likelihood rating (O) by the impact rating (S), i.e. {text{R}} = {text{O}} times {text{S}}.
The criticality level of a rolling stock failure is calculated by multiplying the likelihood of occurrence of the failure mode (O) and the severity of damage caused by the failure (S), each being rated with a number from 1 to 10 (1 = lowest, 10 = highest).
The data duplication is reduced (say "semi-expanded") if there are multiple deaths on the same day by multiplying the likelihood contribution from that day by the number of deaths on the case day (weighting).
During fitting for the network model, the likelihood function is computed by multiplying the likelihood functions for the 5-h and the 22-h simulations computed as described above, and the fitting procedure seeks the value c that maximized the value of the combined likelihood function.
To illustrate the effects of multiplying the likelihood by a prior, suppose that in the 1 × 2 example in Figure 2 the likelihoods of the tiles in Hypothesis 5 and Hypothesis 6 were the same, both equal to some value l.
The problem of graduates avoiding local government jobs for their lack of prestige may be remedied by the ongoing anti-corruption campaign, which has reduced some of the most conspicuous nepotism and multiplied the likelihood of punishments for accepting bribes and other offenses.
This multiplies the likelihood for errors and system vulnerabilities.
Finally, we multiplied the likelihood ratio (LR) from independent SNPs, incorporated it with the pre-test probability to estimate the post-test probability of the disease.
When defining this similarity score, we consider the likelihood that the column of the first motif's PFM can be produced by the column of the second motif's PSSM, and multiply the likelihood by the information content of the column of the second motif's PSSM, and vise versa.
Armed with a reasonable assessment of pre-test odds, the framework of evidence based medicine, which has been taught in medical schools and in residency programs for decades, simply multiplies the likelihood ratios of disease state, given the results of the tests, to produce a post-test odds of disease.
