Estimating the normal-inverse-Wishart distribution
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arXiv
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| Format: | Preprint |
| Published: |
2024
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| _version_ | 1866914820465360896 |
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| author | So, Jonathan |
| author_facet | So, Jonathan |
| contents | The normal-inverse-Wishart (NIW) distribution is commonly used as a prior distribution for the mean and covariance parameters of a multivariate normal distribution. The family of NIW distributions is also a minimal exponential family. In this short note we describe a convergent procedure for converting from mean parameters to natural parameters in the NIW family, or -- equivalently -- for performing maximum likelihood estimation of the natural parameters given observed sufficient statistics. This is needed, for example, when using a NIW base family in expectation propagation. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2405_16088 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Estimating the normal-inverse-Wishart distribution So, Jonathan Statistics Theory Machine Learning The normal-inverse-Wishart (NIW) distribution is commonly used as a prior distribution for the mean and covariance parameters of a multivariate normal distribution. The family of NIW distributions is also a minimal exponential family. In this short note we describe a convergent procedure for converting from mean parameters to natural parameters in the NIW family, or -- equivalently -- for performing maximum likelihood estimation of the natural parameters given observed sufficient statistics. This is needed, for example, when using a NIW base family in expectation propagation. |
| title | Estimating the normal-inverse-Wishart distribution |
| topic | Statistics Theory Machine Learning |
| url | https://arxiv.org/abs/2405.16088 |