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Section reference: https://immm-sfa.github.io/msd_uncertainty_ebook/docs/html/A1_Uncertainty_Quantification.html#markov-chain-monte-carlo

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A general workflow for MCMC is shown in [Fig. A.4](https://immm-sfa.github.io/msd_uncertainty_ebook/docs/html/A1_Uncertainty_Quantification.html#figure-a1-4). The first decision is whether to use the full model or a surrogate model (or emulator). Typical surrogates include Gaussian process emulation [[173](https://immm-sfa.github.io/msd_uncertainty_ebook/docs/html/R.Bibliography.html#id172), [174](https://immm-sfa.github.io/msd_uncertainty_ebook/docs/html/R.Bibliography.html#id173)], polynomial chaos expansions [[175](https://immm-sfa.github.io/msd_uncertainty_ebook/docs/html/R.Bibliography.html#id174), [176](https://immm-sfa.github.io/msd_uncertainty_ebook/docs/html/R.Bibliography.html#id175)], support vector machines :cite:p`ciccazzo_svm_2016, pruett_creation_2016`, and neural networks [[177](https://immm-sfa.github.io/msd_uncertainty_ebook/docs/html/R.Bibliography.html#id178), [178](https://immm-sfa.github.io/msd_uncertainty_ebook/docs/html/R.Bibliography.html#id179)]. Surrogate modeling can be faster, but requires a sufficient number of model evaluations for the surrogate to accurately represent the model’s response surface, and this typically limits the number of parameters which can be included in the analysis.