Parameter estimation II

In the previous chapter, we were concerned with the most elementary type of data analysis problem: parameter estimation involving just one unknown variable. Now let us progress to the more common case in which there are several parameters; some of these will be of interest to us, but others merely a...

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Bibliographic Details
Published inData Analysis
Main Author Sivia, D.S.
Format Book Chapter
LanguageEnglish
Published Oxford Oxford University Press 01.06.2006
Subjects
Applied Mathematics
Mathematical and Statistical Physics
Probability and Statistics
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common
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approximations
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Summary:In the previous chapter, we were concerned with the most elementary type of data analysis problem: parameter estimation involving just one unknown variable. Now let us progress to the more common case in which there are several parameters; some of these will be of interest to us, but others merely a nuisance. As well as being a more challenging optimization problem, we will be led to generalize the idea of error-bars to include correlations and to the use of marginalization to deal with the unwanted variables. We will also see how certain approximations naturally give rise to some of the most frequently used analysis procedures, and discuss the so-called propagation of errors.
ISBN:9780198568315
0198568312
DOI:10.1093/oso/9780198568315.003.0003