The prior probability reflects how probable the expert thinks the model is before looking at the data [63].
We first define a Retaining Rate for a position on transcript, which is a probability indicates how probable the position of the probe on transcript remains after two-cycle amplification.
Then, for each transcript's products, we could estimate a Retaining Rate function p i (z), i = 1,2, which states a probability indicates how probable the the nucleotide on the position of z (bp) retains after the incomplete synthesis in the ith cycle amplification.
The probability density function specifies how probable scores are by the height of the function, and the best-known example of a density function is the famous normal density, the "bell" curve.
First, each individual filter in is updated using standard measurement update methods, for example, a KF, and then the probability is updated according to how probable that mode is given the measurement, (4).
The chemical master equation describes the time evolution of the system state probability distribution, i.e. how probable it is that a chemical species in the system will have specific particle numbers at a specific point in time.
Although such models can be used satisfactorily to infer how probable events are, they are not stable enough to predict how probabilities would change as a result of external interventions [ 3, 4].
In the BMA of the case study, the prior probability concerns the intuition of the uncertainty analyst how probable she believes a model M γ might be before looking at the data.
The conditional probability Pα[A | (B·C)] completely discounts the possibility that B is false, whereas the probability of the conditional Pα[(B⊃A) | C] depends significantly on how probable B is (given C), and must approach 1 if Pα[B | C] is near 0. Rule (5*) captures how this difference between the conditional probability and the probability of a conditional works.
This score measures how probable is for the gene to truly belong to GO‐BP, given its probability estimate output from SVM, and also given the precision value of SVM for the particular category.
The Bayes classification[28] is based on estimating the prior probabilities π i for each class which describe the prior estimates about how probable a class is.
