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<div>Hi, Adapters:<br>
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<div>Today, I will give a brief talk about probabilistic graphical model. <span>Probabilistic graphical models (PGMs) are a rich framework for encoding probability distributions over complex domains: joint (multivariate) distributions over large numbers of
random variables that interact with each other. These representations sit at the intersection of statistics and computer science, relying on concepts from probability theory, graph algorithms, machine learning, and more.</span></div>
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<p class="ecxMsoNormal"><span lang="EN-US"><span>They are the basis for the state-of-the-art methods in a wide variety of applications, such as medical diagnosis, image understanding, speech recognition, natural language processing, and many, many more. They
are also a foundational tool in formulating many machine learning problems. </span>
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<p class="ecxMsoNormal"><span lang="EN-US"><span>In this seminar, i will pick some basic knowledge to make you understanding this framework better.</span></span></p>
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<p class="ecxMsoNormal"><span lang="EN-US"><span>Thanks,</span></span></p>
<p class="ecxMsoNormal"><span lang="EN-US">Yi Liu (Áõµt)</span></p>
<p class="ecxMsoNormal"><span lang="EN-US">-------------------------<br>
Department of Computer Science and Engineering <br>
Shanghai Jiao Tong University<br>
800 Dongchuan Road, Shanghai, China<br>
Email: </span><span lang="EN-US">liuyi61ly@hotmail.com</span></p>
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