This book offers a comprehensive framework for mastering the complexities of learning high-dimensional sparse graphical models through the use of conditional independence tests. These tests are ...

Model-based clustering is popularly used in statistical literature, which often models the data with a Gaussian mixture model. As a consequence, it requires estimation of a large amount of parameters, ...

Empirical Bayes is a versatile approach to “learn from a lot” in two ways: first, from a large number of variables and, second, from a potentially large amount of prior information, for example, ...

Earth system box models are essential tools for reconstructing long-term climatic and environmental evolution and uncovering ...

At its heart, data modeling is about understanding how data flows through a system. Just as a map can help us understand a city’s layout, data modeling can help us understand the complexities of a ...