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| Main Author: | |
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| Format: | Preprint |
| Published: |
2024
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2406.01819 |
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| _version_ | 1866911479919280128 |
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| author | Christen, J Andres |
| author_facet | Christen, J Andres |
| contents | I present all the details in calculating the posterior distribution of the conjugate Normal-Gamma prior in Bayesian Linear Models (BLM), including correlated observations, prediction, model selection and comments on efficient numeric implementations. A Python implementation is also presented. These have been presented and available in many books and texts but, I believe, a general compact and simple presentation is always welcome and not always simple to find. Since correlated observations are also included, these results may also be useful for time series analysis and spacial statistics. Other particular cases presented include regression, Gaussian processes and Bayesian Dynamic Models. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2406_01819 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Bayesian Linear Models: A compact general set of results Christen, J Andres Methodology 62F15 I present all the details in calculating the posterior distribution of the conjugate Normal-Gamma prior in Bayesian Linear Models (BLM), including correlated observations, prediction, model selection and comments on efficient numeric implementations. A Python implementation is also presented. These have been presented and available in many books and texts but, I believe, a general compact and simple presentation is always welcome and not always simple to find. Since correlated observations are also included, these results may also be useful for time series analysis and spacial statistics. Other particular cases presented include regression, Gaussian processes and Bayesian Dynamic Models. |
| title | Bayesian Linear Models: A compact general set of results |
| topic | Methodology 62F15 |
| url | https://arxiv.org/abs/2406.01819 |