Bayesian history matching is a statistical method used to align model predictions with observed data in the context of complex computational models. This approach is particularly useful in fields such as environmental science, engineering, and the social sciences, where models are often computationally intensive and may involve various uncertainties. ### Key Aspects of Bayesian History Matching: 1. **Bayesian Framework**: Bayesian history matching applies Bayes' theorem to update our beliefs about the parameters of a model based on observed data.
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