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Model selection is the process of choosing a statistical or machine learning model from a set of candidate models, based on their performance and suitability for a given dataset or task. It involves balancing model complexity and goodness of fit to optimize predictive accuracy and interpretability.
Gaussian processes are a powerful statistical tool used in machine learning for regression and classification tasks, offering probabilistic predictions.
A Markov random field is a mathematical model used in various fields such as image processing and statistical physics, characterized by its Markov property over a network of random variables.