Once there is a timeout, the
posterior probability in the tth stage is calculated for MIDG, which can be denoted as [P.sup.(t+1)] ([[theta].sub.S] = 1| [a.sub.S] (t +1)).
These species are located under the A1 haplogroup with a high bootstrap support (100%) and
posterior probability (1.00) (Figure 2).
Our MCMC simulation estimated a mean
posterior probability of occupancy of 0.109 (95% credible interval 0.000-0.761).
Obviously, the larger the F(I([y.sub.k])) is, the higher
posterior probability of target existence is.
The Bayesian network could be used to calculate the
posterior probability of risk factors under conditions of an accident and obtain the most likely factors or combinations that caused accidents.
Model Equation [R.sup.2] BIC 1 CY=47628.43+48.76*MP -22.96*Yr -44.1*T 0.039 -6.233 2 CY=45415.62-21.79*Yr -44.19*T 0.028 -5.481 3 CY=47844.53+48.86*MP -23.68*Yr 0.027 -5.091 4 CY=45627.45 -22.52*Yr 0.017 -4.389 5 CY=46660.38-27.52M+50.87MP -23.03*Yr 0.034 -3.15 Model
Posterior probability 1 0.311 2 0.214 3 0.176 4 0.124 5 0.067 Table 3: Estimated coefficients.
with p([X.sub.1]) is Priori probability (or marginal, or occurrence probability) of event [X.sub.1] it is prior in the sense that it does not take into account any information about [X.sub.2], p([X.sub.2]) is Marginal probabilities of event [X.sub.2], p([X.sub.1] / [X.sub.2]) is
Posterior probability (or conditional probability) of [X.sub.1] knowing [X.sub.2], p([X.sub.2]/[X.sub.1]) is Likelihood function (or conditional probability) of [X.sub.2] knowing [X.sub.1].
The
posterior probability of every operating mode can be computed based on Bayes' rule.