MARC状态：已编 文献类型：西文图书 浏览次数：24
- 题名/责任者:
- Practical Bayesian inference : a primer for physical scientists / Coryn A.L. Bailer-Jones, Max Planck Institute for Astronomy, Heidelberg.
- 出版发行项:
- Cambridge, United Kingdom ; New York, NY : Cambridge University Press, 2017.
- ISBN:
- 9781108129312
- ISBN:
- 9781316642214
- 载体形态项:
- 1 online resource (ix, 295 pages) : illustrations
- 个人责任者:
- Bailer-Jones, Coryn A. L., author.
- 论题主题:
- Mathematical physics.
- 中图法分类号:
- O212.8
- 一般附注:
- Description based on print version record.
- 书目附注:
- Includes bibliographical references (pages 289-209) and index.
- 内容附注:
- 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.
- 摘要附注:
- "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"--
- 随书光盘:
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