160 related articles for article (PubMed ID: 31283513)
1. An Efficient Entropy-Based Causal Discovery Method for Linear Structural Equation Models With IID Noise Variables.
Xie F; Cai R; Zeng Y; Gao J; Hao Z
IEEE Trans Neural Netw Learn Syst; 2020 May; 31(5):1667-1680. PubMed ID: 31283513
[TBL] [Abstract][Full Text] [Related]
2. Causality in linear nongaussian acyclic models in the presence of latent gaussian confounders.
Chen Z; Chan L
Neural Comput; 2013 Jun; 25(6):1605-41. PubMed ID: 23517099
[TBL] [Abstract][Full Text] [Related]
3. Causal Discovery in Linear Non-Gaussian Acyclic Model With Multiple Latent Confounders.
Chen W; Cai R; Zhang K; Hao Z
IEEE Trans Neural Netw Learn Syst; 2022 Jul; 33(7):2816-2827. PubMed ID: 33417571
[TBL] [Abstract][Full Text] [Related]
4. A multivariate additive noise model for complete causal discovery.
Parida PK; Marwala T; Chakraverty S
Neural Netw; 2018 Jul; 103():44-54. PubMed ID: 29626732
[TBL] [Abstract][Full Text] [Related]
5. A causal discovery algorithm based on the prior selection of leaf nodes.
Zeng Y; Hao Z; Cai R; Xie F; Ou L; Huang R
Neural Netw; 2020 Apr; 124():130-145. PubMed ID: 31991308
[TBL] [Abstract][Full Text] [Related]
6. On the Role of Entropy-Based Loss for Learning Causal Structure With Continuous Optimization.
Chen W; Qiao J; Cai R; Hao Z
IEEE Trans Neural Netw Learn Syst; 2023 Nov; PP():. PubMed ID: 37943646
[TBL] [Abstract][Full Text] [Related]
7. Non-Gaussian Methods for Causal Structure Learning.
Shimizu S
Prev Sci; 2019 Apr; 20(3):431-441. PubMed ID: 29789997
[TBL] [Abstract][Full Text] [Related]
8. Application of quantum computing to a linear non-Gaussian acyclic model for novel medical knowledge discovery.
Kawaguchi H
PLoS One; 2023; 18(4):e0283933. PubMed ID: 37018292
[TBL] [Abstract][Full Text] [Related]
9. FOM: Fourth-order moment based causal direction identification on the heteroscedastic data.
Cai R; Ye J; Qiao J; Fu H; Hao Z
Neural Netw; 2020 Apr; 124():193-201. PubMed ID: 32018157
[TBL] [Abstract][Full Text] [Related]
10. Bayesian Estimation of Causal Direction in Acyclic Structural Equation Models with Individual-specific Confounder Variables and Non-Gaussian Distributions.
Shimizu S; Bollen K
J Mach Learn Res; 2014 Aug; 15():2629-2652. PubMed ID: 31402848
[TBL] [Abstract][Full Text] [Related]
11. Testability of Instrumental Variables in Linear Non-Gaussian Acyclic Causal Models.
Xie F; He Y; Geng Z; Chen Z; Hou R; Zhang K
Entropy (Basel); 2022 Apr; 24(4):. PubMed ID: 35455175
[TBL] [Abstract][Full Text] [Related]
12. Pairwise Likelihood Ratios for Estimation of Non-Gaussian Structural Equation Models.
Hyvärinen A; Smith SM
J Mach Learn Res; 2013 Jan; 14(Jan):111-152. PubMed ID: 31695580
[TBL] [Abstract][Full Text] [Related]
13. A pooling-LiNGAM algorithm for effective connectivity analysis of fMRI data.
Xu L; Fan T; Wu X; Chen K; Guo X; Zhang J; Yao L
Front Comput Neurosci; 2014; 8():125. PubMed ID: 25339895
[TBL] [Abstract][Full Text] [Related]
14. Causal discovery and inference: concepts and recent methodological advances.
Spirtes P; Zhang K
Appl Inform (Berl); 2016; 3():3. PubMed ID: 27195202
[TBL] [Abstract][Full Text] [Related]
15. Estimating Causal Effects with Ancestral Graph Markov Models.
Malinsky D; Spirtes P
JMLR Workshop Conf Proc; 2016 Aug; 52():299-309. PubMed ID: 28217244
[TBL] [Abstract][Full Text] [Related]
16. Denoising of polychromatic CT images based on their own noise properties.
Kim JH; Chang Y; Ra JB
Med Phys; 2016 May; 43(5):2251. PubMed ID: 27147337
[TBL] [Abstract][Full Text] [Related]
17. EEG adaptive noise cancellation using information theoretic approach.
Darroudi A; Parchami J; Razavi MK; Sarbisheie G
Biomed Mater Eng; 2017; 28(4):325-338. PubMed ID: 28869426
[TBL] [Abstract][Full Text] [Related]
18. Causal Discovery Combining K2 with Brain Storm Optimization Algorithm.
Hong Y; Hao Z; Mai G; Huang H; Kumar Sangaiah A
Molecules; 2018 Jul; 23(7):. PubMed ID: 30012940
[TBL] [Abstract][Full Text] [Related]
19. Learning Subject-Specific Directed Acyclic Graphs With Mixed Effects Structural Equation Models From Observational Data.
Li X; Xie S; McColgan P; Tabrizi SJ; Scahill RI; Zeng D; Wang Y
Front Genet; 2018; 9():430. PubMed ID: 30333854
[TBL] [Abstract][Full Text] [Related]
20. Estimating exogenous variables in data with more variables than observations.
Sogawa Y; Shimizu S; Shimamura T; Hyvärinen A; Washio T; Imoto S
Neural Netw; 2011 Oct; 24(8):875-80. PubMed ID: 21719253
[TBL] [Abstract][Full Text] [Related]
[Next] [New Search]