修正目录

docs/20.md

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 >示例:
 > *   一个用 [`GaussianMixture`](https://scikit-learn.org/stable/modules/generated/sklearn.mixture.GaussianMixture.html#sklearn.mixture.GaussianMixture "sklearn.mixture.GaussianMixture") 和 [`BayesianGaussianMixture`](https://scikit-learn.org/stable/modules/generated/sklearn.mixture.BayesianGaussianMixture.html#sklearn.mixture.BayesianGaussianMixture "sklearn.mixture.BayesianGaussianMixture") 绘制置信椭圆体的例子， 请查阅 [Gaussian Mixture Model Ellipsoids](https://scikit-learn.org/stable/auto_examples/mixture/plot_gmm.html#sphx-glr-auto-examples-mixture-plot-gmm-py)
 > *   [Gaussian Mixture Model Sine Curve](https://scikit-learn.org/stable/auto_examples/mixture/plot_gmm_sin.html#sphx-glr-auto-examples-mixture-plot-gmm-sin-py) 这个例子展示了用 [`GaussianMixture`](https://scikit-learn.org/stable/modules/generated/sklearn.mixture.GaussianMixture.html#sklearn.mixture.GaussianMixture "sklearn.mixture.GaussianMixture") 和 [`BayesianGaussianMixture`](https://scikit-learn.org/stable/modules/generated/sklearn.mixture.BayesianGaussianMixture.html#sklearn.mixture.BayesianGaussianMixture "sklearn.mixture.BayesianGaussianMixture") 来拟合正弦波。
-> *   一个使用不同的 `weight_concentration_prior_type` 用以不同的 `weight_concentration_prior` 参数值的:class:*BayesianGaussianMixture* 来绘制置信椭圆体的例子。 请查阅 [Concentration Prior Type Analysis of Variation Bayesian Gaussian Mixture](https://scikit-learn.org/stable/auto_examples/mixture/plot_concentration_prior.html#sphx-glr-auto-examples-mixture-plot-concentration-prior-py)
+> *   一个使用不同的 `weight_concentration_prior_type` 用以不同的 `weight_concentration_prior` 参数值的 *BayesianGaussianMixture* 来绘制置信椭圆体的例子。 请查阅 [Concentration Prior Type Analysis of Variation Bayesian Gaussian Mixture](https://scikit-learn.org/stable/auto_examples/mixture/plot_concentration_prior.html#sphx-glr-auto-examples-mixture-plot-concentration-prior-py)
 
 ### 2.1.2.2. [`BayesianGaussianMixture`](https://scikit-learn.org/stable/modules/generated/sklearn.mixture.BayesianGaussianMixture.html#sklearn.mixture.BayesianGaussianMixture "sklearn.mixture.BayesianGaussianMixture") 下的变分推理的优缺点