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Titlebook: Unobserved Variables; Models and Misunders David J. Bartholomew Book 2013 The Author(s) 2013 Bayesian methods.Latent variables.Missing data

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樓主
發(fā)表于 2025-3-21 18:19:17 | 只看該作者 |倒序瀏覽 |閱讀模式
書目名稱Unobserved Variables
副標題Models and Misunders
編輯David J. Bartholomew
視頻videohttp://file.papertrans.cn/943/942388/942388.mp4
概述Includes supplementary material:
叢書名稱SpringerBriefs in Statistics
圖書封面Titlebook: Unobserved Variables; Models and Misunders David J. Bartholomew Book 2013 The Author(s) 2013 Bayesian methods.Latent variables.Missing data
描述?The classical statistical problem typically involves a probability distribution which depends on a number of unknown parameters. The form of the distribution may be known, partially or completely, and inferences have to be made on the basis of a sample of observations drawn from the distribution; often, but not necessarily, a random sample. This brief deals with problems where some of the sample members are either unobserved or hypothetical, the latter category being introduced as a means of better explaining the data. Sometimes we are interested in these kinds of variable themselves and sometimes in the parameters of the distribution. Many problems that can be cast into this form are treated. These include: missing data, mixtures, latent variables, time series and social measurement problems. Although all can be accommodated within a Bayesian framework, most are best treated from first principles.
出版日期Book 2013
關鍵詞Bayesian methods; Latent variables; Missing data; Mixtures; Time Series
版次1
doihttps://doi.org/10.1007/978-3-642-39912-1
isbn_softcover978-3-642-39911-4
isbn_ebook978-3-642-39912-1Series ISSN 2191-544X Series E-ISSN 2191-5458
issn_series 2191-544X
copyrightThe Author(s) 2013
The information of publication is updating

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沙發(fā)
發(fā)表于 2025-3-21 20:27:30 | 只看該作者
Unobserved Variables,hese methods has given rise to many misunderstandings which, we shall argue, often arise because the need for a statistical, or probability, model is unrecognised or disregarded. A statistical model is the bridge between intuition and the analysis of data.
板凳
發(fā)表于 2025-3-22 02:12:25 | 只看該作者
Models for Ability,theory. Maximum likelihood estimation and notions of sufficiency also appear in a central role which is developed in later chapters. A good deal of misunderstanding and controversy has surrounded the Rasch model and we hope some of this may be dispelled by the present approach.
地板
發(fā)表于 2025-3-22 06:19:37 | 只看該作者
5#
發(fā)表于 2025-3-22 12:48:19 | 只看該作者
Social Measurement,ing the circumstances under which the simple measures in common use may be used legitimately. Our treatment of the former requires the introduction of the hazard function of a probability function for a positive random variable and the renewal rate, both of which are defined.
6#
發(fā)表于 2025-3-22 13:46:32 | 只看該作者
Simple Mixtures,en if it is theoretically possible to make the distinction, it may be very difficult to do so in practice. Mixtures of normal and exponential distributions are both common and important and the mathematical simplicity of the latter makes them an ideal vehicle for exploring some of the fundamental issues.
7#
發(fā)表于 2025-3-22 19:07:46 | 只看該作者
8#
發(fā)表于 2025-3-23 00:53:14 | 只看該作者
2191-544X of unknown parameters. The form of the distribution may be known, partially or completely, and inferences have to be made on the basis of a sample of observations drawn from the distribution; often, but not necessarily, a random sample. This brief deals with problems where some of the sample members
9#
發(fā)表于 2025-3-23 03:22:36 | 只看該作者
10#
發(fā)表于 2025-3-23 08:13:35 | 只看該作者
Measurement, Estimation and Prediction,s is because the quantities to be ‘measured’ turn out to be represented by the parameters or random variables of a statistical model. Measurement then becomes a matter of predicting the values of random variables or of estimating the parameters of a distribution. When the unobserved variables are la
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