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Bayesian machine learning techniques belong to a class of methods in statistical inference, where the emphasis is on Bayesian probability theory. The key feature of Bayesian techniques is that they provide a probabilistic approach to inference, which allows for a more nuanced understanding of model uncertainty and the incorporation of prior knowledge into the model. Here’s where Bayesian techniques generally fit into the broader context of machine learning applications:

• Feature Engineering:
  • Bayesian methods can be used to select features that are most likely to improve the performance of predictive models based on posterior probabilities. This is especially useful in scenarios where the relationship between features and outcomes is uncertain or when data is sparse.
• Model Selection and Hyperparameter Tuning:
  • Bayesian optimization is a strategy for model selection and the tuning of hyperparameters. It uses the Bayesian technique of setting a prior over the objective function and combining it with evidence to get a posterior function. This can be particularly useful for finding the optimal model settings in a more computationally efficient way.
• Uncertainty Quantification:
  • Bayesian models naturally incorporate uncertainty in their predictions, providing not just estimates but also the confidence in these estimates. This is crucial for decision-making processes where risk assessment is important, such as in quality control or during the development of new semiconductor materials or processes.
• Probabilistic Predictions:
  • In scenarios where it is beneficial to understand the probability distribution of outcomes rather than just making point predictions, Bayesian methods excel. For example, in predictive maintenance, not only predicting when a machine might fail but also understanding the probability distribution of the failure time can be more actionable.
• Adaptive Learning:
  • Bayesian methods are inherently adaptive, which makes them well suited for applications where models need to dynamically update as new data becomes available. This feature is particularly valuable in fast-changing environments such as semiconductor manufacturing processes.

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