Application of the Bayesian Method in Statistical Modeling
Bayesian statistics is a theory in the field of statistics based on the Bayesian interpretation of probability, where probability expresses a degree of belief in an event. Bayesian statistical methods use Bayes' theorem to compute and update probabilities after obtaining new data. Named after Thomas...
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| Formatua: | Online |
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| Hizkuntza: | ingelesa |
| Argitaratua: |
MDPI - Multidisciplinary Digital Publishing Institute
2025
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| Sarrera elektronikoa: | ONIX_20250812T110751_9783725834990_44 |
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| _version_ | 1869521036904497152 |
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| collection | Directory of Open Access Books |
| description | Bayesian statistics is a theory in the field of statistics based on the Bayesian interpretation of probability, where probability expresses a degree of belief in an event. Bayesian statistical methods use Bayes' theorem to compute and update probabilities after obtaining new data. Named after Thomas Bayes, Bayes' theorem (1973) describes the conditional probability of an event based on data, as well as prior information or beliefs about the event or conditions related to the event. This approach differs from other interpretations of probability, such as the frequentist interpretation, which views probability as the limit of the relative frequency of an event after many trials. During much of the 20th century, many statisticians viewed Bayesian methods unfavorably due primarily to practical considerations. Bayesian methods required much computation to complete, and the most widely used methods during the previous century relied on frequentist interpretation. However, with the advent of powerful computers and new algorithms, such as Markov chain Monte Carlo, Bayesian methods have seen increasing use within statistics in the 21st century. This Special Issue will raise awareness of the availability and applicability of Bayesian analyses. It includes a collection of theoretical and applied studies using Bayesian statistics and provides information on statistical software that allows the use of Bayesian estimation methods. |
| format | Online |
| id | doab-20.500.12854ir-165288 |
| institution | Directory of Open Access Books |
| language | eng |
| publishDate | 2025 |
| publishDateRange | 2025 |
| publishDateSort | 2025 |
| publisher | MDPI - Multidisciplinary Digital Publishing Institute |
| publisherStr | MDPI - Multidisciplinary Digital Publishing Institute |
| record_format | ojs |
| spelling | doab-20.500.12854ir-1652882025-08-12T09:17:51Z Application of the Bayesian Method in Statistical Modeling Mindrila, Diana Bayesian analysis Bayesian estimation Statistics probability thema EDItEUR::G Reference, Information and Interdisciplinary subjects::GP Research and information: general thema EDItEUR::P Mathematics and Science Bayesian statistics is a theory in the field of statistics based on the Bayesian interpretation of probability, where probability expresses a degree of belief in an event. Bayesian statistical methods use Bayes' theorem to compute and update probabilities after obtaining new data. Named after Thomas Bayes, Bayes' theorem (1973) describes the conditional probability of an event based on data, as well as prior information or beliefs about the event or conditions related to the event. This approach differs from other interpretations of probability, such as the frequentist interpretation, which views probability as the limit of the relative frequency of an event after many trials. During much of the 20th century, many statisticians viewed Bayesian methods unfavorably due primarily to practical considerations. Bayesian methods required much computation to complete, and the most widely used methods during the previous century relied on frequentist interpretation. However, with the advent of powerful computers and new algorithms, such as Markov chain Monte Carlo, Bayesian methods have seen increasing use within statistics in the 21st century. This Special Issue will raise awareness of the availability and applicability of Bayesian analyses. It includes a collection of theoretical and applied studies using Bayesian statistics and provides information on statistical software that allows the use of Bayesian estimation methods. 2025-08-12T09:17:49Z 2025-08-12T09:17:49Z 2025 book ONIX_20250812T110751_9783725834990_44 9783725834990 9783725835003 https://directory.doabooks.org/handle/20.500.12854/165288 eng image/jpeg Attribution 4.0 International https://mdpi.com/books https://mdpi.com/books/pdfview/book/11014 MDPI - Multidisciplinary Digital Publishing Institute 10.3390/books978-3-7258-3500-3 10.3390/books978-3-7258-3500-3 46cabcaa-dd94-4bfe-87b4-55023c1b36d0 9783725834990 9783725835003 284 open access |
| spellingShingle | Bayesian analysis Bayesian estimation Statistics probability thema EDItEUR::G Reference, Information and Interdisciplinary subjects::GP Research and information: general thema EDItEUR::P Mathematics and Science Application of the Bayesian Method in Statistical Modeling |
| title | Application of the Bayesian Method in Statistical Modeling |
| title_full | Application of the Bayesian Method in Statistical Modeling |
| title_fullStr | Application of the Bayesian Method in Statistical Modeling |
| title_full_unstemmed | Application of the Bayesian Method in Statistical Modeling |
| title_short | Application of the Bayesian Method in Statistical Modeling |
| title_sort | application of the bayesian method in statistical modeling |
| topic | Bayesian analysis Bayesian estimation Statistics probability thema EDItEUR::G Reference, Information and Interdisciplinary subjects::GP Research and information: general thema EDItEUR::P Mathematics and Science |
| topic_facet | Bayesian analysis Bayesian estimation Statistics probability thema EDItEUR::G Reference, Information and Interdisciplinary subjects::GP Research and information: general thema EDItEUR::P Mathematics and Science |
| url | ONIX_20250812T110751_9783725834990_44 |