## KEYI LIUI am a Master of Science student in the Department of Electrical & Computer System Engineering, Rensselaer Polytechnique Institute. Currently, I am in the Intelligent System Lab located at JEC. I got my Bachelor's degree in Jilin University, Changchun, Jilin, China. My undergraduate major is the Internet of Things Engineering in the College of Computer Science and Technology. My interest is Probabilistic Graphical models and how to apply those graphical models to real world applications in Computer Vision. |

Probabilistic Graphical Models

**Learning Deep generative Causal model
**

Now, data is the most crucial source that we have at hand, so people try to find some patterns among the data, however, there might be some hidden causes that generate these data. Nowadays, vast majority of the deep models are used for discovering correlation relationship among random variables, like DBM and DBN. Since the correlation relationship does not imply the causal relationship, when there is prior knowledge indicating that causal relationship exists, correlation relationship discovery may not capture this property well. This motivates us to extract this causal relationship by constructing a deep causal network in which the NoBN is the building block.
When learning the latent-node generative model, learning also needs inference. The problem for inference is that doing exact posterior inferece is intractable when network is large. So, my current research focuses on this part.

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