From: Hierarchical Bayesian adaptive lasso methods on exponential random graph models
Mean of the MCMC output as the estimate for \({\theta }\) | ||||||||
---|---|---|---|---|---|---|---|---|
 |  | True Value \(^{a}\) | Estimate \(^{b}\) | Quantiles\(^{c}\) | ||||
 |  |  |  | 2.5% | 25% | 50% | 75% | 97.5% |
BERGM | \(\theta _{1}\) | −4.800 | −5.3470 | −5.607 | −5.456 | −5.349 | −5.253 | −5.031 |
\(\theta _{2}\) | 2.300 | 5.2656 | 4.455 | 5.026 | 5.251 | 5.543 | 6.185 | |
BALERGM | \(\theta _{1}\) | −4.800 | −4.9444 | −5.172 | −5.018 | −4.938 | −4.871 | −4.739 |
\(\theta _{2}\) | 2.300 | 2.8550 | 2.496 | 2.680 | 2.824 | 2.996 | 3.304 | |
Median of the MCMC output as the estimate for \(\varvec{\theta }\) | ||||||||
 |  | True Value | Estimate \(^{d}\) | Quantiles | ||||
 |  | 2.5% | 25% | 50% | 75% | 97.5% | ||
BERGM | \(\theta _{1}\) | −4.800 | −5.1367 | −5.386 | −5.235 | −5.137 | −5.045 | −4.874 |
\(\theta _{2}\) | 2.300 | 4.6296 | 3.903 | 4.397 | 4.632 | 4.822 | 5.355 | |
BALERGM | \(\theta _{1}\) | −4.800 | −4.8778 | −5.077 | −4.949 | −4.873 | −4.806 | −4.682 |
\(\theta _{2}\) | 2.300 | 2.6131 | 2.398 | 2.501 | 2.595 | 2.713 | 2.918 |