Comparative investigation of three Bayesian p values
Bayesian p values are a popular and important class of approaches for Bayesian model checking. They are used to quantify the degree of surprise from the observed data given the specified data model and prior distribution. A systematic investigation is conducted to compare three Bayesian p values — t...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Zhang, Junni L. [verfasserIn] |
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| Format: |
E-Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2014transfer abstract |
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| Schlagwörter: |
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| Umfang: |
15 |
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| Übergeordnetes Werk: |
Enthalten in: An Orthopaedic Pre-operative Skin Decolonization Protocol Process Improvement Project at an Academic Medical Center - Phillips, Eileen ELSEVIER, 2014, Amsterdam |
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| Übergeordnetes Werk: |
volume:79 ; year:2014 ; pages:277-291 ; extent:15 |
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10.1016/j.csda.2014.05.012 |
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| 520 | |a Bayesian p values are a popular and important class of approaches for Bayesian model checking. They are used to quantify the degree of surprise from the observed data given the specified data model and prior distribution. A systematic investigation is conducted to compare three Bayesian p values — the posterior predictive p value, the sampled posterior p value and the calibrated posterior predictive p value. Their general computation costs are compared, and several examples that incorporate both simple and complex Bayesian models are used to compare their frequency properties. It is recommended to use the sampled posterior p value because it is computationally least expensive and safest. | ||
| 520 | |a Bayesian p values are a popular and important class of approaches for Bayesian model checking. They are used to quantify the degree of surprise from the observed data given the specified data model and prior distribution. A systematic investigation is conducted to compare three Bayesian p values — the posterior predictive p value, the sampled posterior p value and the calibrated posterior predictive p value. Their general computation costs are compared, and several examples that incorporate both simple and complex Bayesian models are used to compare their frequency properties. It is recommended to use the sampled posterior p value because it is computationally least expensive and safest. | ||
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10.1016/j.csda.2014.05.012 doi GBVA2014005000012.pica (DE-627)ELV033727805 (ELSEVIER)S0167-9473(14)00152-2 DE-627 ger DE-627 rakwb eng 004 004 DE-600 610 VZ 540 VZ 35.18 bkl Zhang, Junni L. verfasserin aut Comparative investigation of three Bayesian p values 2014transfer abstract 15 nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier Bayesian p values are a popular and important class of approaches for Bayesian model checking. They are used to quantify the degree of surprise from the observed data given the specified data model and prior distribution. A systematic investigation is conducted to compare three Bayesian p values — the posterior predictive p value, the sampled posterior p value and the calibrated posterior predictive p value. Their general computation costs are compared, and several examples that incorporate both simple and complex Bayesian models are used to compare their frequency properties. It is recommended to use the sampled posterior p value because it is computationally least expensive and safest. Bayesian p values are a popular and important class of approaches for Bayesian model checking. They are used to quantify the degree of surprise from the observed data given the specified data model and prior distribution. A systematic investigation is conducted to compare three Bayesian p values — the posterior predictive p value, the sampled posterior p value and the calibrated posterior predictive p value. Their general computation costs are compared, and several examples that incorporate both simple and complex Bayesian models are used to compare their frequency properties. It is recommended to use the sampled posterior p value because it is computationally least expensive and safest. Causal effect Elsevier Bayesian model checking Elsevier Posterior predictive p value Elsevier Calibrated posterior predictive p value Elsevier Hierarchical model Elsevier Sampled posterior p value Elsevier Enthalten in Elsevier Science Phillips, Eileen ELSEVIER An Orthopaedic Pre-operative Skin Decolonization Protocol Process Improvement Project at an Academic Medical Center 2014 Amsterdam (DE-627)ELV022563539 volume:79 year:2014 pages:277-291 extent:15 https://doi.org/10.1016/j.csda.2014.05.012 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U GBV_ILN_130 35.18 Kolloidchemie Grenzflächenchemie VZ AR 79 2014 277-291 15 045F 004 |
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Bayesian p values are a popular and important class of approaches for Bayesian model checking. They are used to quantify the degree of surprise from the observed data given the specified data model and prior distribution. A systematic investigation is conducted to compare three Bayesian p values — the posterior predictive p value, the sampled posterior p value and the calibrated posterior predictive p value. Their general computation costs are compared, and several examples that incorporate both simple and complex Bayesian models are used to compare their frequency properties. It is recommended to use the sampled posterior p value because it is computationally least expensive and safest. |
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Bayesian p values are a popular and important class of approaches for Bayesian model checking. They are used to quantify the degree of surprise from the observed data given the specified data model and prior distribution. A systematic investigation is conducted to compare three Bayesian p values — the posterior predictive p value, the sampled posterior p value and the calibrated posterior predictive p value. Their general computation costs are compared, and several examples that incorporate both simple and complex Bayesian models are used to compare their frequency properties. It is recommended to use the sampled posterior p value because it is computationally least expensive and safest. |
| abstract_unstemmed |
Bayesian p values are a popular and important class of approaches for Bayesian model checking. They are used to quantify the degree of surprise from the observed data given the specified data model and prior distribution. A systematic investigation is conducted to compare three Bayesian p values — the posterior predictive p value, the sampled posterior p value and the calibrated posterior predictive p value. Their general computation costs are compared, and several examples that incorporate both simple and complex Bayesian models are used to compare their frequency properties. It is recommended to use the sampled posterior p value because it is computationally least expensive and safest. |
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| title_short |
Comparative investigation of three Bayesian p values |
| url |
https://doi.org/10.1016/j.csda.2014.05.012 |
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| doi_str |
10.1016/j.csda.2014.05.012 |
| up_date |
2024-07-06T19:17:55.495Z |
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