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The calculated posterior statistics were then used to calculate probabilistic fatigue life estimates of sample laminates.
During the calculation, computational difficulties associated with calculating the posterior statistics are addressed.
The posterior statistics can then be calculated using weighted averaging of the posterior sigma-points, (15).
After, convergence and accuracy of posterior estimates are attained and summarizing the posterior statistics is achievable.
Unlike randomized gossip algorithms[30], this procedure diffuses information by randomly propagating posterior statistics across the network.
This implies convergence and accuracy of posterior estimates are attained and the model is appropriate to estimate posterior statistics.
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The deterministic trend model is adopted, and MCMC iterations 25,000-26,000 are cumulated over the nine models, providing posterior summary statistics (mean, variance, 2.5% percentile, 97.5% percentile) for cross-validatory predicted extents in 2010-11 across the entire Arctic region.
The modelling approach allows for inspection of a range of posterior distribution statistics.
Posterior summary statistics were based on 3 Markov chains of 20,000 lengths after a burn-in period of 20,000 iterations.
MCMC chain length was set to 5 × 10 to ensure effective sample sizes were sufficient for strong posterior distribution statistics.
Table 4 displays posterior summary statistics for the regression coefficients related to the SOFA score course together with the dropout processes.
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