Nodal support is Bayesian
posterior probability (PP) and maximum likelihood bootstrap (BS) values obtained from Bayesian inference analysis and maximum likelihood analysis, respectively.
In this case, the
posterior probability of lack of dental caries is equal to 19%.
In this un-normalized form, the
posterior probability [rho]([theta]|y,x) of parameter, [theta], given data, y, and constant, x, is proportional (for fixed y and x) to the product of the likelihood function [rho](y|[theta],x) and prior [rho]([theta],x) (Stan Development Team 2016).
The LCA classes were given the following descriptive labels based on observed VA utilization: (1) Low VA medical use with minimal VA medication and mental health use (representing an expected 43 percent of patients based on posterior probabilities from the LCA model; 45.4 percent based on assigning patients to the most probable class); (2) Low VA medical users with significant VA medication and mental health use (12.7 percent mean
posterior probability); (3) Moderate VA medical use with minimalVA medication and mental health use (23.1 percent); (4) Moderate VA medical use with significant VA medication and mental health use (10.5 percent); and (5) High use of all VA services including inpatient hospitalizations (10.8 percent).
The
posterior probability is strictly calculated by the prior probability and Bayesian rules;
The BA tree is presented in Figure 2, with
posterior probability (BA) and bootstrap support (ML) values.
We also estimated the
posterior probability of detection using a flat beta-binomial prior with the observed data using 5000 iterations of a Markov Chain Monte Carlo (MCMC) simulation in the computer program Winbugs version 1.4.3 (Lunn and others 2000).
We assign a miRNA to cluster k * if the
posterior probability [p.sub.gk*] is the largest among the 3 posterior probabilities, [p.sub.g1], [p.sub.g2], and [p.sub.g3].
Further, the
posterior probability of I([y.sub.k]) can be calculated according to Bayesian theory
where P(X) is called the prior probability and P(Y | X) is the
posterior probability. Combined with the chain rules, reducing the complexity of the probability model, the joint distribution of n variables is
The most appropriate model was chosen based on the lowest BIC and highest
posterior probability. Analyses were performed with RStudio for Windows.