Discovering Symptom Co-Occurrence Patterns from 604 Cases of Depressive Patient Data Using Latent Tree Models

2014 ◽  
Vol 20 (4) ◽  
pp. 265-271 ◽  
Author(s):  
Yan Zhao ◽  
Nevin L. Zhang ◽  
Tianfang Wang ◽  
Qingguo Wang
Keyword(s):  
Patient Data ◽  
Depressive Patient ◽  
Latent Tree Models ◽  
Tree Models ◽  
Occurrence Patterns

scholarly journals UC-LTM: Unidimensional clustering using latent tree models for discrete data

2018 ◽  
Vol 92 ◽  
pp. 392-409
Author(s):  
Leonard K.M. Poon ◽  
April H. Liu ◽  
Nevin L. Zhang
Keyword(s):  
Discrete Data ◽  
Latent Tree Models ◽  
Tree Models

Greedy learning of latent tree models for multidimensional clustering

2013 ◽  
Vol 98 (1-2) ◽  
pp. 301-330 ◽  
Author(s):  
Teng-Fei Liu ◽  
Nevin L. Zhang ◽  
Peixian Chen ◽  
April Hua Liu ◽  
Leonard K. M. Poon ◽  
...  
Keyword(s):  
Latent Tree Models ◽  
Tree Models

scholarly journals Latent Tree Models and Approximate Inference in Bayesian Networks

2008 ◽  
Vol 32 ◽  
pp. 879-900 ◽  
Author(s):  
Y. Wang ◽  
N. L. Zhang ◽  
T. Chen
Keyword(s):  
Bayesian Networks ◽  
Linear Time ◽  
Computational Cost ◽  
Approximate Inference ◽  
Tree Model ◽  
Approximation Accuracy ◽  
Latent Tree Models ◽  
Tree Models ◽  
Novel Method ◽  
Latent Tree Model

We propose a novel method for approximate inference in Bayesian networks (BNs). The idea is to sample data from a BN, learn a latent tree model (LTM) from the data offline, and when online, make inference with the LTM instead of the original BN. Because LTMs are tree-structured, inference takes linear time. In the meantime, they can represent complex relationship among leaf nodes and hence the approximation accuracy is often good. Empirical evidence shows that our method can achieve good approximation accuracy at low online computational cost.

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