In recent years, significant advances have been made in the design and evaluation of
balanced (hyper) graph partitioning algorithms. We survey trends of the past decade in …

Probabilistic modeling is iterative. A scientist posits a simple model, fits it to her data, refines
it according to her analysis, and repeats. However, fitting complex models to large data is a …

Tree boosting has empirically proven to be a highly effective approach to predictive
modeling. It has shown remarkable results for a vast array of problems. For many years …

This book is a graduate-level introduction to probabilistic programming. It not only provides a
thorough background for anyone wishing to use a probabilistic programming system, but …

To describe the underlying processes involved in oceanic plankton dynamics is crucial for
the determination of energy and mass flux through an ecosystem and for the estimation of …

The success of the open-source statistical software “R” has made a significant impact on the
teaching and research of statistics in the last decade. Analysing data is now easier and more …

Modern astronomical research is beset with a vast range of statistical challenges, ranging
from reducing data from megadatasets to characterizing an amazing variety of variable …

In this definitive book, DR Cox gives a comprehensive and balanced appraisal of statistical
inference. He develops the key concepts, describing and comparing the main ideas and …

Meaning is a fundamental component of nearly all aspects of human cognition, but formal
models of semantic memory have classically lagged behind many other areas of cognition …

Though there are many recent additions to graduate-level introductory books on Bayesian
analysis, none has quite our blend of theory, methods, and ap plications. We believe a …