Bayesian Astrophysics Bayesian Astrophysics
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Lời Giới Thiệu Của Nhà Xuất Bản

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.

THỂ LOẠI
Khoa Học & Tự Nhiên
ĐÃ PHÁT HÀNH
2018
21 tháng 4
NGÔN NGỮ
EN
Tiếng Anh
ĐỘ DÀI
333
Trang
NHÀ XUẤT BẢN
Cambridge University Press
NGƯỜI BÁN
Cambridge University Press
KÍCH THƯỚC
22,7
Mb
Astrostatistical Challenges for the New Astronomy Astrostatistical Challenges for the New Astronomy
2012
Statistical Methods for Astronomical Data Analysis Statistical Methods for Astronomical Data Analysis
2014
Bayesian Statistics from Methods to Models and Applications Bayesian Statistics from Methods to Models and Applications
2015
Advanced Statistical Methods for Astrophysical Probes of Cosmology Advanced Statistical Methods for Astrophysical Probes of Cosmology
2013
Statistics and its Applications Statistics and its Applications
2018
Topics in Nonparametric Statistics Topics in Nonparametric Statistics
2014
Cosmic Magnetic Fields Cosmic Magnetic Fields
2018
High Time-Resolution Astrophysics High Time-Resolution Astrophysics
2018
Astrophysical Applications of Gravitational Lensing Astrophysical Applications of Gravitational Lensing
2016