Bayesian Methods In Cosmology

Comprehensive introduction to Bayesian methods in cosmological studies, for graduate students and researchers in cosmology, astrophysics and applied statistics.

Statistical literacy is critical for the modern researcher in Physics and Astronomy. This book empowers researchers in these disciplines by providing the tools they will need to analyze their own data. Chapters in this book provide a statistical base from which to approach new problems, including numerical advice and a profusion of examples. The examples are engaging analyses of real-world problems taken from modern astronomical research. The examples are intended to be starting points for readers as they learn to approach their own data and research questions. Acknowledging that scientific progress now hinges on the availability of data and the possibility to improve previous analyses, data and code are distributed throughout the book. The JAGS symbolic language used throughout the book makes it easy to perform Bayesian analysis and is particularly valuable as readers may use it in a myriad of scenarios through slight modifications. This book is comprehensive, well written, and will surely be regarded as a standard text in both astrostatistics and physical statistics. Joseph M. Hilbe, President, International Astrostatistics Association, Professor Emeritus, University of Hawaii, and Adjunct Professor of Statistics, Arizona State University

"Bayesian methods are increasingly being employed in many different areas of physical sciences research. In astrophysics, models are used to make predictions to compare to observations that are incomplete and uncertain, so the comparison must be pursued by following a probabilistic approach. With contributions from leading experts, this volume covers the foundations of Bayesian inference, a description of the applicable computational methods, and recent results from their application to areas such as exoplanet detection and characterisation, image reconstruction, and cosmology. With content that appeals both to young researchers seeking to learn about Bayesian methods and to astronomers wishing to incorporate these approaches into their research, it provides the next generation of researchers with tools of modern data analysis that are becoming standard in astrophysical research"--

A hands-on guide to Bayesian models with R, JAGS, Python, and Stan code, for a wide range of astronomical data types.

Digital sky surveys, high-precision astrometry from satellite data, deep-space data from orbiting telescopes, and the like have all increased the quantity and quality of astronomical data by orders of magnitude per year for several years. Making sense of this wealth of data requires sophisticated statistical techniques. Fortunately, statistical methodologies have similarly made great strides in recent years. Powerful synergies thus emerge when astronomers and statisticians join in examining astrostatistical problems and approaches. The book begins with an historical overview and tutorial articles on basic cosmology for statisticians and the principles of Bayesian analysis for astronomers. As in earlier volumes in this series, research contributions discussing topics in one field are joined with commentary from scholars in the other. Thus, for example, an overview of Bayesian methods for Poissonian data is joined by discussions of planning astronomical observations with optimal efficiency and nested models to deal with instrumental effects. The principal theme for the volume is the statistical methods needed to model fundamental characteristics of the early universe on its largest scales.

This thesis explores advanced Bayesian statistical methods for extracting key information for cosmological model selection, parameter inference and forecasting from astrophysical observations. Bayesian model selection provides a measure of how good models in a set are relative to each other - but what if the best model is missing and not included in the set? Bayesian Doubt is an approach which addresses this problem and seeks to deliver an absolute rather than a relative measure of how good a model is. Supernovae type Ia were the first astrophysical observations to indicate the late time acceleration of the Universe - this work presents a detailed Bayesian Hierarchical Model to infer the cosmological parameters (in particular dark energy) from observations of these supernovae type Ia.

'The past cultures of astronomy and physics evolved their own distinct personalities. The book describes an important milestone in the history of the unification of the two fields and provides an excellent summary of the methods used to explore one of the greatest mysteries in physics today: dark energy.'Physics TodayThis book is about the Dark Energy Survey, a cosmological experiment designed to investigate the physical nature of dark energy by measuring its effect on the expansion history of the universe and on the growth of large-scale structure. The survey saw first light in 2012, after a decade of planning, and completed observations in 2019. The collaboration designed and built a 570-megapixel camera and installed it on the four-metre Blanco telescope at the Cerro Tololo Inter-American Observatory in the Chilean Andes. The survey data yielded a three-dimensional map of over 300 million galaxies and a catalogue of thousands of supernovae. Analysis of the early data has confirmed remarkably accurately the model of cold dark matter and a cosmological constant. The survey has also offered new insights into galaxies, supernovae, stellar evolution, solar system objects and the nature of gravitational wave events.A project of this scale required the long-term commitment of hundreds of scientists from institutions all over the world. The chapters in the first three sections of the book were either written by these scientists or based on interviews with them. These chapters explain, for a non-specialist reader, the science analysis involved. They also describe how the project was conceived, and chronicle some of the many and diverse challenges involved in advancing our understanding of the universe. The final section is trans-disciplinary, including inputs from a philosopher, an anthropologist, visual artists and a poet. Scientific collaborations are human endeavours and the book aims to convey a sense of the wider context within which science comes about.This book is addressed to scientists, decision makers, social scientists and engineers, as well as to anyone with an interest in contemporary cosmology and astrophysics.Related Link(s)

This book includes the lectures given during the third session of the School of Statistics for Astrophysics that took place at Autrans, near Grenoble, in France, in October 2017. The subject is Bayesian Methodology. The interest of this statistical approach in astrophysics probably comes from its necessity and its success in determining the cosmological parameters from observations, especially from the cosmic background luctuations. The cosmological community has thus been very active in this field for many years. But the Bayesian methodology, complementary to the more classical frequentist one, has many applications in physics in general due to its ability to incorporate a priori knowledge into inference, such as uncertainty brought by the observational processes. The Bayesian approach becomes more and more widespread in the astrophysical literature. This book contains statistics courses on basic to advanced methods with practical exercises using the R environment, by leading experts in their field. This covers the foundations of Bayesian inference, Markov chain Monte Carlo technique, model building, Approximate Bayesian Computation (ABC) and Bayesian nonparametric inference and clustering.