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):
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.
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.