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Titlebook: Bayesian Methods for the Physical Sciences; Learning from Exampl Stefano Andreon,Brian Weaver Book 2015 Springer Nature Switzerland AG 2015

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31#
發(fā)表于 2025-3-27 00:38:58 | 只看該作者
32#
發(fā)表于 2025-3-27 02:44:48 | 只看該作者
Carsten Carlberg,Ferdinand Molnár some specific complexity pointed out by the data? Furthermore, are the data informative about the quantity being measured or are results sensibly dependent on details of the fitted model? And, finally, what about if assumptions are uncertain? A number of examples illustrate how to answer these questions.
33#
發(fā)表于 2025-3-27 07:18:24 | 只看該作者
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發(fā)表于 2025-3-27 09:27:50 | 只看該作者
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發(fā)表于 2025-3-27 16:46:15 | 只看該作者
Bayesian vs Simple Methods,n terms of quality of the prediction, accuracy of the estimates, and fairness and noisiness of the quoted errors. We also focus on three failures of maximum likelihood methods occurring with small samples, with mixtures, and with regressions with errors in the predictor quantity.
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發(fā)表于 2025-3-27 21:35:40 | 只看該作者
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發(fā)表于 2025-3-27 22:13:56 | 只看該作者
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發(fā)表于 2025-3-28 03:49:50 | 只看該作者
39#
發(fā)表于 2025-3-28 10:19:02 | 只看該作者
Fitting Regression Models,ip and estimate any unknown parameters that dictate this relationship. Questions of interest include: how to deal with samples affected by selection effects? How does a rich data structure influence the fitted parameters? And what about non-linear multiple-predictor fits, upper/lower limits, measure
40#
發(fā)表于 2025-3-28 11:06:55 | 只看該作者
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