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<oembed><version>1.0</version><provider_name>Glosarix</provider_name><provider_url>https://glosarix.com/en/</provider_url><author_name>Team Glosarix</author_name><author_url>https://glosarix.com/en/author/adm_glosarix/</author_url><title>Mixture Models - Glosarix</title><type>rich</type><width>600</width><height>338</height><html>&lt;blockquote class="wp-embedded-content" data-secret="HuPF3bmI03"&gt;&lt;a href="https://glosarix.com/en/glossary/mixture-models-en/"&gt;Mixture Models&lt;/a&gt;&lt;/blockquote&gt;&lt;iframe sandbox="allow-scripts" security="restricted" src="https://glosarix.com/en/glossary/mixture-models-en/embed/#?secret=HuPF3bmI03" width="600" height="338" title="&#x201C;Mixture Models&#x201D; &#x2014; Glosarix" data-secret="HuPF3bmI03" frameborder="0" marginwidth="0" marginheight="0" scrolling="no" class="wp-embedded-content"&gt;&lt;/iframe&gt;&lt;script&gt;
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</html><description>Description: A mixture model is a probabilistic model that assumes that data is generated from a mixture of several distributions. These models are fundamental in the field of unsupervised learning, as they allow for the identification of hidden patterns in the data without the need for labels. The central idea is that each component of [&hellip;]</description></oembed>
