Bayesian Astrophysics

Description

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

Andres Asensio Ramos is an astrophysicist working as a researcher at the Instituto de Astrofisica de Canarias, Tenerife, Spain. where he also completed his Ph.D. His current research focuses on the interpretation of spectropolarimetric signals in the Sun and other stars to infer properties about the magnetic field. His interests in astrophysics are spectropolarimetry, solar and stellar physics and Bayesian inference. Inigo Arregui is an astrophysicist working at the Instituto de Astrofisica de Canarias. He completed a Ph.D. in Physics at Universitat de les Illes Balears with a thesis on MHD waves in the solar atmosphere. His research focuses on the interpretation and modeling of wave activity in the solar atmosphere, designing tools for remote sensing of solar atmospheric plasmas, and the study of wave based plasma heating mechanisms.