dorsal/arxiv
View SchemaA Good Measure for Bayesian Inference
| Authors | Hanns L. Harney |
|---|---|
| Categories | |
| ArXiv ID | physics/0103030 |
| URL | https://arxiv.org/abs/physics/0103030 |
Abstract
The Gaussian theory of errors has been generalized to situations, where the Gaussian distribution and, hence, the Gaussian rules of error propagation are inadequate. The generalizations are based on Bayes' theorem and a suitable measure. The following text sketches some chapters of a monograph that is presently prepared. We concentrate on the material that is - to the best of our knowledge - not yet in the statistical literature. See especially the extension of form invariance to discrete data in section 4, the criterion on the compatibility between a proposed distribution and sparse data in section 7 and the ``discovery'' of probability amplitudes in section 9.
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"abstract": "The Gaussian theory of errors has been generalized to situations, where the\nGaussian distribution and, hence, the Gaussian rules of error propagation are\ninadequate. The generalizations are based on Bayes\u0027 theorem and a suitable\nmeasure. The following text sketches some chapters of a monograph that is\npresently prepared. We concentrate on the material that is - to the best of our\nknowledge - not yet in the statistical literature. See especially the extension\nof form invariance to discrete data in section 4, the criterion on the\ncompatibility between a proposed distribution and sparse data in section 7 and\nthe ``discovery\u0027\u0027 of probability amplitudes in section 9.",
"arxiv_id": "physics/0103030",
"authors": [
"Hanns L. Harney"
],
"categories": [
"physics.data-an"
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"title": "A Good Measure for Bayesian Inference",
"url": "https://arxiv.org/abs/physics/0103030"
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