| 书目名称 | Model Based Inference in the Life Sciences |
| 副标题 | A Primer on Evidence |
| 编辑 | David R. Anderson |
| 视频video | |
| 概述 | Very broad applicability, very science-based, and practical.Very powerful – the concept of formal “strength of evidence”.Simple to use and understand.An emphasis on science philosophy, not just “data |
| 图书封面 |  |
| 描述 | .The abstract concept of “information” can be quantified and this has led to many important advances in the analysis of data in the empirical sciences. This text focuses on a science philosophy based on “multiple working hypotheses” and statistical models to represent them. The fundamental science question relates to the empirical evidence for hypotheses in this set—a formal strength of evidence. Kullback-Leibler information is the information lost when a model is used to approximate full reality. Hirotugu Akaike found a link between K-L information (a cornerstone of information theory) and the maximized log-likelihood (a cornerstone of mathematical statistics). This combination has become the basis for a new paradigm in model based inference. The text advocates formal inference from all the hypotheses/models in the a priori set—multimodel inference...This compelling approach allows a simple ranking of the science hypothesis and their models. Simple methods are introduced for computing the likelihood of model .i,. given the data; the probability of model .i., given the data; and evidence ratios. These quantities represent a formal strength of evidence and are easy to compute and un |
| 出版日期 | Textbook 2008 |
| 关键词 | Akaike’s information criterion AIC; Master Patient Index; Model based inference; Quantitative evidence; |
| 版次 | 1 |
| doi | https://doi.org/10.1007/978-0-387-74075-1 |
| isbn_softcover | 978-0-387-74073-7 |
| isbn_ebook | 978-0-387-74075-1 |
| copyright | Springer-Verlag New York 2008 |
发表于 2025-3-21 19:06:12
