If users submit a new training sample [T'.sub.k] and merge it with the original training sample Tk, the prior probability
P[(u,v,w) S[C.sub.k]] shall be re-calculated through Formula (16):
We could specify the reporting probability using additional parameters in these works, for instance, the predefined prior probability
threshold [sigma] in .
According to the definition of the LR, any scenario given a nonzero prior probability
by the DM can influence the value of the LR and is therefore relevant; scenarios given a prior probability
of zero cannot influence the value of the LR regardless of the value of the corresponding likelihood Pr[y|x, [H.sub.j]], j = 1,..., N, and are therefore irrelevant to the DM.
The results in Tables 4 and 5 indicate that the prior probability
of occurrence of a landslide (surge) in the reservoir is 0.0387.
(2) P([D.sub.i]) is the prior probability
of [D.sub.i] (the prior probability
of occurrence of disease);
Contrary to Johnson, assessing the prior probability
that the database
The calculation of posterior probability for the alternative model requires a prior probability
distribution for the effect size for the alternative model.
A probability model comprising prior probability
and likelihood can be trained through practical application using Bayesian updating.
Since the sum of all the elements in x is 1 and its ith element [[omega].sub.i] represents the relative importance of the state [S.sub.i] among all the states, it is natural to interpret [[omega].sub.i] as the prior probability
of stat [S.sub.i].
More generally, assume that there is a prior probability
P([[omega].sub.k]) of each subclass k.
The input quantities for Bayesian inference are on the left-hand side of (1) and are as follows: p([theta] | I) is the prior probability
for a hypothesis about the values of the source parameter vector [theta] which encodes our state of knowledge about these parameters before the receipt of the concentration data d and p(d | [theta],1) is the likelihood function and is considered to be a function of [theta] for fixed data d.
The Obtaining of the Prior Probability
Density Function Based on Noise Level Estimation.