This course equips learners with the theoretical knowledge and computational skills needed to implement modern Bayesian statistical methods in real-world settings. By completing the course, learners ...

Gaussian process (GP) regression is a popular surrogate modeling tool for computer simulations in engineering and scientific domains. However, it often struggles with high computational costs and low ...

Computational statistics combines mathematical theory, algorithm design and high-performance computing to extract insight from data. It spans simulation-based inference (for example, bootstrap and ...

Bayesian model selection provides a coherent framework for comparing competing statistical models by balancing goodness of fit against model complexity through the use of prior distributions and ...

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Offered through an interdisciplinary partnership, data science at CU Boulder is delivered by the Departments of Applied Mathematics, Computer Science, and Information Science and awarded by the ...