Issue 614: [DOCS] Add bayesian methods to GMM density page #642
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…ine mentioning the available models
koaning
reviewed
Mar 23, 2024
docs/user-guide/mixture-methods.md
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| @@ -4,7 +4,7 @@ Gaussian Mixture Models (GMMs) are flexible building blocks for other machine le | |||
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| This is in part because they are great approximations for general probability distributions but also because they remain somewhat interpretable even when the dataset gets very complex. | |||
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| This package makes use of GMMs to construct other algorithms. | |||
| This package makes use of GMMs to construct other algorithms. Bayesian [BayesianGMMClassifier][bayes_gmm-classifier-api] and non-Bayesian Gaussian Mixture Models [GMMClassifier][gmm-classifier-api] are available. | |||
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I might rephrase this slightly because at this point in the docs folks are already aware of the normal GMMClassifier.
Maybe something like "In addition to the GMMClassifier this library also features a Bayesian variant. More information can be found here"?
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Updated, thank you! |
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I think it would make sense to mention |
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Fixes issue #614: [DOCS] Add bayesian methods to GMM density pageAdded a line mentioning the available models
Before working on a large PR, please check with @koaning or @MBrouns that they agree with the direction of the PR. This discussion should take place in a Github issue before working on the PR, unless it's a minor change like spelling in the docs.
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