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Analytical Posterior Model Probabilities are displayed in the red line.

Based on this resulting posterior model, forward simulator is used to generate predicted posterior model values for the future mining operations (for next two days).

Fig. 2 Posterior Model Size Distribution and Posterior Model Probabilities: model size reflects the number of variables suggested by prior and posterior distribution of potential variables (mean 7.003).

In Fig. 2, model size distribution and the posterior model probabilities are shown.

The ensuing posterior model is mined through based on production data.

Thirdly, BMA provides the best performing model in terms of its posterior model probability (PMP).

The posterior model probability (PMP) can be used to derive the structural, model dependent uncertainty.

These are derived from the COV-OBS.x1 field model and its posterior model error covariance matrix.

The operation file mines through the posterior model and highlights the area as green.

The standard procedure in such a case is to use the expected likelihood in computing the posterior model probabilities.

The significant computational cost of evaluating posterior model probabilities renders traditional Bayesian methods infeasible when the model space becomes large.