The phrase "a minimal probability" is correct and usable in written English.
It can be used in contexts involving statistics, risk assessment, or any situation where you are discussing the likelihood of an event occurring at a very low level.
Example: "In this experiment, we found a minimal probability of error, suggesting that our results are highly reliable."
Alternatives: "a slight chance" or "a low likelihood".
This continuous measure can be combined using further simple functions: mean returns the mean probability of success by the three classifiers [17]; min yields the minimal probability of success (a pessimistic measure); max results in the maximal predicted probability of success (an optimistic measure); median returns the median probability.
Success in molecular biology techniques involving hybridization such as PCR and non-PCR-based techniques depends on annealing temperature prediction which derived itself from the melting temperature (Tm) of primers with a minimal error probability.
In order to identify cases in which RNA extracted from FFPE was not of sufficient quality for nanoString to confidently assign subgroup, we established a minimal PAM probability threshold, proceeding only with those FFPE samples where PAM provided a high confidence subgroup assignment.
The efficiency of the proposed algorithms relies on the assumption of a given constant, the cumulative weight threshold, defined as the minimal probability of occurrence of factors in the weighted sequence.
To achieve the necessary long-term virus suppression, we believe that CARs must be designed for optimal potency and anti-HIV specificity, and also for minimal probability of virus escape and CAR immunogenicity.
We can partition the support of the distribution of f into regions R k in which the kth factor is critical (for simplicity in the argument ignoring ties, which have minimal probability) so that eg for any ( f 1 x j, f 2 x j,.. f Kx j ) ∈ R k, f kx j = min ( f 1 x j, f 2 x j,.. f Kx j ).
The Bayes error corresponds to the minimal probability of classification error for any given distributions [33], [34], that is, the probability that a sample is assigned to a wrong class [10].
