Publisher's Synopsis
Physicists, when modelling physical systems with a large number of degrees of freedom,
and statisticians, when performing data analysis, have developed their own concepts and
methods for making the `best' inference. There are mathematical similarities between the
inference problems in statistics and statistical physics, and the viewpoint from statistical
physics can help the quantitative understanding of inference problems. Over the last few
years, it has been increasingly realised that ideas from statistical physics of disordered
systems can help to develop new algorithms for important inference problems in different
fields of application.
This interdisciplinary field between statistical physics and statistics is now attracting much
attention, but there is as yet no summarizing books to capture this synergy. This book will
help researchers interested in the application of statistical inference and will enhance further development in statistical physics and statistics by presenting a review of the
present landscape. It explains how the analytical tools of statistical physics can be
exploited in the understanding wider inference problems. The authors describe how
important statistical problems including maximum likelihood estimation, Bayesian
inference, sparse estimation, information criterion and model selection can be mapped
onto the statistical physics view and how the analytical tools of statistical physics can be
used for solving such problems.
Key Features:
- First book on the topic
- Self-contained
- Mathematically accessible by grad students
- Author team of physicist and statistician
