245 10 |a Practical Bayesian inference : |b a primer for physical scientists / |c Coryn A.L. Bailer-Jones, Max Planck Institute for Astronomy, Heidelberg.

264 _1 |a Cambridge, United Kingdom ; |a New York, NY : |b Cambridge University Press, |c 2017.

505 0_ |a Probability basics -- Estimation and uncertainty -- Statistical models and inference -- Linear models, least squares, and maximum likelihood -- Parameter estimation: single parameter -- Parameter estimation: multiple parameters -- Approximating distributions -- Monte Carlo methods for inference -- Parameter estimation: Markov Chain Monte Carlo -- Frequentist hypothesis testing -- Model comparison -- Dealing with more complicated problems.

520 __ |a "Science is fundamentally about learning from data, and doing so in the presence of uncertainty. Uncertainty arises inevitably and avoidably in many guises. It comes from noise in our measurements: we cannot measure exactly. It comes from sampling effects: we cannot measure everything. It comes from complexity: data may be numerous, high dimensional, and correlated, making it difficult to see structures. This book is an introduction to statistical methods for analysing data. It presents the major concepts of probability and statistics as well as the computational tools we need to extract meaning from data in the presence of uncertainty"-- |c Provided by publisher.

856 4_ |u http://www.itextbook.cn/f/book/bookDetail?bookId=f6a87a7430da4d359e664cdaf30bb96c |z An electronic book accessible through the World Wide Web; click to view