pgm模型_PGM的应用以及贝叶斯和马尔可夫模型的关系和转换

pgm模型

PGM的应用:(用例) (Application of PGMs: (Use cases))

  • Netflix, Amazon, facebook all use PGM recommend what is best for you.

    Netflix,Amazon,facebook都使用PGM推荐最适合您的。

  • This algorithm, Use the latent factor model and non-negative matrix factorization.

    该算法使用潜在因子模型和非负矩阵分解。

  • In politics American presidential polls using PGMs for prediction. FiveThirtyEight company that makes a prediction about American presidential polls using PGMs.

    在政治上,美国总统选举使用PGM进行预测。 FiveThirtyEight公司使用PGM对美国总统选举进行预测的公司。

  • PGM is also inferred whether one is a MODI and KEJRIWAL supporter.

    还可以推断PGM是否是MODI和KEJRIWAL支持者。

How Bayes network and Markov Random Fields Model related to each other:

贝叶斯网络和马尔可夫随机场模型如何相互关联:

For P(A, B) = P(A) * P(B/A) For P(A,B) α Ф(A,B)
For chain: P(A,B) = P(A) P(B/A) P(C/B) For chain: P(A,B) α Ф(A,B) Ф(B,C)
Shared parents: P(A,B,C) = P(A) P(B/A) P(C/B) Shared parents: P(A,B,C) α Ф(A,B) Ф(A,C)
Ф(A,B) ← P(A) P(B\A)
Ф(A,C) ← P(C\A)
Two parent shared a child: P(A,B,C) = P(A) P(B) P(C\A,B) A and B are independent given C P(A,B,C) α Ф(A,C) Ф(B,C)
对于P(A,B)= P(A)* P(B / A) 对于P(A,B)αФ(A,B)
对于链:P(A,B)= P(A)P(B / A)P(C / B) 对于链条:P(A,B)αФ(A,B)Ф(B,C)
共享父母:P(A,B,C)= P(A)P(B / A)P(C / B) 共有父母:P(A,B,C)αФ(A,B)Ф(A,C)
Ф(A,B)←P(A)P(B \ A)
Ф(A,C)←P(C \ A)
两个父母共享一个孩子:P(A,B,C)= P(A)P(B)P(C \ A,B) 给定CP(A,B,C)αФ(A,C)Ф(B,C),A和B是独立的

Converting Bayes network into Markov random fields:

将贝叶斯网络转换为马尔可夫随机字段:

Moralizing parents: P (A, B, C) α Ф(A,C) Ф(B,C) where A and B are independent given C

道德父母:P(A,B,C)αФ(A,C)Ф(B,C)其中A和B是给定的独立C

  • Moralize all co-parents

    道德化所有同父母

  • More challenging is that Lose marginal independence of parents.

    更具挑战性的是失去父母的边缘独立性。

    pgm模型_PGM的应用以及贝叶斯和马尔可夫模型的关系和转换_第1张图片

Basically, it given an interference class of all directed graphical models and a class of all undirected graphical model are different but overlap each other.

基本上,它给定了所有有向图模型的干扰类别,而所有无向图模型的类别都不同但彼此重叠。

Conclusion: In this article we have learnt about application of PGMs, how bayes network is related to markov's random field and conversion of bayes network into Markov's network model.

结论:在本文中,我们了解了PGM的应用 ,贝叶斯网络与马可夫随机场的关系以及贝叶斯网络到马尔可夫网络模型的转换。

翻译自: https://www.includehelp.com/ml-ai/applications-of-pgms-and-relation-and-conversion-of-bayes-and-markovs-model.aspx

pgm模型

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