Zobrazeno 1 - 10
of 95
pro vyhledávání: '"Hanns Ludwig Harney"'
Publikováno v:
Mathematics and Statistics. 10:690-700
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::386f08e5492c0866897d53ac5c62a6e2
https://doi.org/10.13189/ms.2022.100326
https://doi.org/10.13189/ms.2022.100326
The present article derives the minimal number $N$ of observations needed to consider a Bayesian posterior distribution as Gaussian. Two examples are presented. Within one of them, a chi-squared distribution, the observable $x$ as well as the paramet
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::7793d23360615c8a71bd5d8edcb7c7c8
http://arxiv.org/abs/2012.00748
http://arxiv.org/abs/2012.00748
Autor:
Hanns Ludwig Harney
This new edition offers a comprehensive introduction to the analysis of data using Bayes rule. It generalizes Gaussian error intervals to situations in which the data follow distributions other than Gaussian. This is particularly useful when the obse
Autor:
Hanns Ludwig Harney
Publikováno v:
Bayesian Inference ISBN: 9783319416427
Item response theory (IRT) tries to construct a statistical model of measurement in psychology.
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::70ba360b125ef097977dcf024fa6ab39
https://doi.org/10.1007/978-3-319-41644-1_12
https://doi.org/10.1007/978-3-319-41644-1_12
Autor:
Hanns Ludwig Harney
Publikováno v:
Bayesian Inference ISBN: 9783319416427
Fitting a set of observations \(\varvec{x} = (x_1,\ldots ,x_N)\) means that one hopes to have a theoretical understanding of the observations and wants to see whether theory and data fit to each other.
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::6b0f7755d3435b6a0d691405b2e5963e
https://doi.org/10.1007/978-3-319-41644-1_13
https://doi.org/10.1007/978-3-319-41644-1_13
Autor:
Hanns Ludwig Harney
Publikováno v:
Bayesian Inference ISBN: 9783319416427
Ignorance about the hypothesis \(\xi \) cannot in general be expressed by the uniform prior. This is a consequence of the transformation law of a probability density discussed in Sect. 2.2. Under a reparameterisation of the hypothesis, the uniform de
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::51a8bd96a59d88bf807b0b5d0b4f5cf5
https://doi.org/10.1007/978-3-319-41644-1_6
https://doi.org/10.1007/978-3-319-41644-1_6
Autor:
Hanns Ludwig Harney
Publikováno v:
Bayesian Inference ISBN: 9783319416427
Bayesian Inference ISBN: 9783642055775
Bayesian Inference ISBN: 9783642055775
In the present chapter, the symmetry of form invariance - introduced in Chap. 6 - is generalised to discrete event variables x.
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::2ce9c5f00c1f44246530de3b42dfa489
https://doi.org/10.1007/978-3-319-41644-1_11
https://doi.org/10.1007/978-3-319-41644-1_11
Autor:
Hanns Ludwig Harney
Publikováno v:
Bayesian Inference ISBN: 9783319416427
Bayesian Inference ISBN: 9783642055775
Bayesian Inference ISBN: 9783642055775
In Sect. 3.1, the Bayesian interval is defined. It contains the probable values of a parameter \(\xi \) and serves as the “error interval” of \(\xi \). It is the basis of decisions because it allows distinguishing between probable and improbable
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::1665d1260b67e03f589927c288f39201
https://doi.org/10.1007/978-3-319-41644-1_3
https://doi.org/10.1007/978-3-319-41644-1_3
Autor:
Hanns Ludwig Harney
Publikováno v:
Bayesian Inference ISBN: 9783319416427
There is a formula that yields the invariant measure of a form-invariant model \(p(x|\xi )\) in a straightforward way without analysis of the symmetry group, in particular without knowledge of the multiplication function. This is useful because the a
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::92603b1fcffb5aa40c107984b15c74f2
https://doi.org/10.1007/978-3-319-41644-1_9
https://doi.org/10.1007/978-3-319-41644-1_9
Autor:
Hanns Ludwig Harney
Publikováno v:
Bayesian Inference ISBN: 9783319416427
Bayesian Inference ISBN: 9783642055775
Bayesian Inference ISBN: 9783642055775
Science does not prove anything. Science infers statements about reality. Sometimes the statements are of stunning precision; sometimes they are rather vague. Science never reaches exact results. Mathematics provides proofs but it is devoid of realit
Externí odkaz:
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https://doi.org/10.1007/978-3-319-41644-1_1
https://doi.org/10.1007/978-3-319-41644-1_1