Bayesian inference for variance factor with maximum entropy prior
Abstract The truncated normal distribution based on the maximum entropy principle is introduced as a prior distribution for the variance factor. In order to determine the the parameters of the distribution an efficient iterative scheme is developed. The Bayes variance factor estimates with the trunc...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Ou, Z. [verfasserIn] |
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| Format: |
E-Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
1993 |
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| Anmerkung: |
© Springer-Verlag 1993 |
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| Übergeordnetes Werk: |
Enthalten in: manuscripta geodaetica - Springer Berlin Heidelberg, 18(1993), 5 vom: Sept., Seite 242-248 |
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| Übergeordnetes Werk: |
volume:18 ; year:1993 ; number:5 ; month:09 ; pages:242-248 |
| Links: |
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| DOI / URN: |
10.1007/BF03655316 |
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SPR057837872 |
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| 520 | |a Abstract The truncated normal distribution based on the maximum entropy principle is introduced as a prior distribution for the variance factor. In order to determine the the parameters of the distribution an efficient iterative scheme is developed. The Bayes variance factor estimates with the truncated normal prior and the inverted gamma prior are discussed. The results show that, after introducing the prior information, the precision of the variance estimator can always be improved. | ||
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10.1007/BF03655316 doi (DE-627)SPR057837872 (SPR)BF03655316-e DE-627 ger DE-627 rakwb eng Ou, Z. verfasserin aut Bayesian inference for variance factor with maximum entropy prior 1993 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Springer-Verlag 1993 Abstract The truncated normal distribution based on the maximum entropy principle is introduced as a prior distribution for the variance factor. In order to determine the the parameters of the distribution an efficient iterative scheme is developed. The Bayes variance factor estimates with the truncated normal prior and the inverted gamma prior are discussed. The results show that, after introducing the prior information, the precision of the variance estimator can always be improved. Enthalten in manuscripta geodaetica Springer Berlin Heidelberg 18(1993), 5 vom: Sept., Seite 242-248 (DE-627)SPR057836175 nnns volume:18 year:1993 number:5 month:09 pages:242-248 https://dx.doi.org/10.1007/BF03655316 X:SPRINGER Resolving-System lizenzpflichtig Volltext SYSFLAG_0 GBV_SPRINGER AR 18 1993 5 09 242-248 |
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10.1007/BF03655316 doi (DE-627)SPR057837872 (SPR)BF03655316-e DE-627 ger DE-627 rakwb eng Ou, Z. verfasserin aut Bayesian inference for variance factor with maximum entropy prior 1993 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Springer-Verlag 1993 Abstract The truncated normal distribution based on the maximum entropy principle is introduced as a prior distribution for the variance factor. In order to determine the the parameters of the distribution an efficient iterative scheme is developed. The Bayes variance factor estimates with the truncated normal prior and the inverted gamma prior are discussed. The results show that, after introducing the prior information, the precision of the variance estimator can always be improved. Enthalten in manuscripta geodaetica Springer Berlin Heidelberg 18(1993), 5 vom: Sept., Seite 242-248 (DE-627)SPR057836175 nnns volume:18 year:1993 number:5 month:09 pages:242-248 https://dx.doi.org/10.1007/BF03655316 X:SPRINGER Resolving-System lizenzpflichtig Volltext SYSFLAG_0 GBV_SPRINGER AR 18 1993 5 09 242-248 |
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10.1007/BF03655316 doi (DE-627)SPR057837872 (SPR)BF03655316-e DE-627 ger DE-627 rakwb eng Ou, Z. verfasserin aut Bayesian inference for variance factor with maximum entropy prior 1993 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Springer-Verlag 1993 Abstract The truncated normal distribution based on the maximum entropy principle is introduced as a prior distribution for the variance factor. In order to determine the the parameters of the distribution an efficient iterative scheme is developed. The Bayes variance factor estimates with the truncated normal prior and the inverted gamma prior are discussed. The results show that, after introducing the prior information, the precision of the variance estimator can always be improved. Enthalten in manuscripta geodaetica Springer Berlin Heidelberg 18(1993), 5 vom: Sept., Seite 242-248 (DE-627)SPR057836175 nnns volume:18 year:1993 number:5 month:09 pages:242-248 https://dx.doi.org/10.1007/BF03655316 X:SPRINGER Resolving-System lizenzpflichtig Volltext SYSFLAG_0 GBV_SPRINGER AR 18 1993 5 09 242-248 |
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10.1007/BF03655316 doi (DE-627)SPR057837872 (SPR)BF03655316-e DE-627 ger DE-627 rakwb eng Ou, Z. verfasserin aut Bayesian inference for variance factor with maximum entropy prior 1993 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Springer-Verlag 1993 Abstract The truncated normal distribution based on the maximum entropy principle is introduced as a prior distribution for the variance factor. In order to determine the the parameters of the distribution an efficient iterative scheme is developed. The Bayes variance factor estimates with the truncated normal prior and the inverted gamma prior are discussed. The results show that, after introducing the prior information, the precision of the variance estimator can always be improved. Enthalten in manuscripta geodaetica Springer Berlin Heidelberg 18(1993), 5 vom: Sept., Seite 242-248 (DE-627)SPR057836175 nnns volume:18 year:1993 number:5 month:09 pages:242-248 https://dx.doi.org/10.1007/BF03655316 X:SPRINGER Resolving-System lizenzpflichtig Volltext SYSFLAG_0 GBV_SPRINGER AR 18 1993 5 09 242-248 |
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10.1007/BF03655316 doi (DE-627)SPR057837872 (SPR)BF03655316-e DE-627 ger DE-627 rakwb eng Ou, Z. verfasserin aut Bayesian inference for variance factor with maximum entropy prior 1993 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Springer-Verlag 1993 Abstract The truncated normal distribution based on the maximum entropy principle is introduced as a prior distribution for the variance factor. In order to determine the the parameters of the distribution an efficient iterative scheme is developed. The Bayes variance factor estimates with the truncated normal prior and the inverted gamma prior are discussed. The results show that, after introducing the prior information, the precision of the variance estimator can always be improved. Enthalten in manuscripta geodaetica Springer Berlin Heidelberg 18(1993), 5 vom: Sept., Seite 242-248 (DE-627)SPR057836175 nnns volume:18 year:1993 number:5 month:09 pages:242-248 https://dx.doi.org/10.1007/BF03655316 X:SPRINGER Resolving-System lizenzpflichtig Volltext SYSFLAG_0 GBV_SPRINGER AR 18 1993 5 09 242-248 |
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bayesian inference for variance factor with maximum entropy prior |
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Bayesian inference for variance factor with maximum entropy prior |
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Abstract The truncated normal distribution based on the maximum entropy principle is introduced as a prior distribution for the variance factor. In order to determine the the parameters of the distribution an efficient iterative scheme is developed. The Bayes variance factor estimates with the truncated normal prior and the inverted gamma prior are discussed. The results show that, after introducing the prior information, the precision of the variance estimator can always be improved. © Springer-Verlag 1993 |
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Abstract The truncated normal distribution based on the maximum entropy principle is introduced as a prior distribution for the variance factor. In order to determine the the parameters of the distribution an efficient iterative scheme is developed. The Bayes variance factor estimates with the truncated normal prior and the inverted gamma prior are discussed. The results show that, after introducing the prior information, the precision of the variance estimator can always be improved. © Springer-Verlag 1993 |
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Abstract The truncated normal distribution based on the maximum entropy principle is introduced as a prior distribution for the variance factor. In order to determine the the parameters of the distribution an efficient iterative scheme is developed. The Bayes variance factor estimates with the truncated normal prior and the inverted gamma prior are discussed. The results show that, after introducing the prior information, the precision of the variance estimator can always be improved. © Springer-Verlag 1993 |
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