.png){kind=icon}
返回
Bayesian inference in processing experimental data: principles and basic applications
.png){kind=icon}
OA
AI
DOI：10.1088/0034-4885/66/9/201.png)
.png){kind=icon}
.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
This paper introduces general ideas and some basic methods of the Bayesian probability theory applied to physics measurements. Our aim is to make the reader familiar, through examples rather than rigorous formalism, with concepts such as the following: model comparison (including the automatic Ockham's Razor filter provided by the Bayesian approach); parametric inference; quantification of the uncertainty about the value of physical quantities, also taking into account systematic effects; role of marginalization; posterior characterization; predictive distributions; hierarchical modelling and hyperparameters; Gaussian approximation of the posterior and recovery of conventional methods, especially maximum likelihood and chi-square fits under well-defined conditions; conjugate priors, transformation invariance and maximum entropy motivated priors; and Monte Carlo (MC) estimates of expectation, including a short introduction to Markov Chain MC methods.
Keyword:
PERIODIC SIGNAL-DETECTION
MAXIMUM-ENTROPY
MONTE-CARLO
STATISTICAL MECHANICS
INFORMATION THEORY
ISNT EVERYONE
AI总结
对已上传原文的论文进行重点信息的提取，主要内容包括：简要概述、研究摘要、背景介绍、关键亮点、图文解析、展望与总结。
期刊
IF:
20.7
论文数:
1.6K
被引数:
2.4W
机构
暂无机构信息
引用论文
.png){kind=icon}
查看更多内容