Practical Learning Directed Acyclic Graphs With General Noise Assumptions

Document Type : Original Article


1 Department of statistics, Faculty of Statistics, Mathematics and Computer, Allameh Tabataba'i University, Tehran, Iran.

2 School of biological sciences, IPM, Tehran, Iran.



Directed Acyclic Graphs are investigated focusing on learning the coefficient matrix via continuous optimization.

We have provided three learning strategies and their corresponding improvements in comparison with former algorithms using some numerical illustrations.

Each method is widely introduced and its corresponding concepts are also studied.

We have extended, the learning assumptions for each strategy.

Lots of preliminary assumptions including normality of noises, and independent and identically distributed errors are removed and with these general considerations, the learning methods are even improved than some existing methods.

Furthermore, the number of new criteria that can evaluate the learning processes are given and throughout simulation studies are analyzed.

Their sensitivity analysis is also presented which can be useful due to the learning power of any presented strategy.

Finally, some further discussions are introduced as guidance for future works.


Main Subjects

Articles in Press, Accepted Manuscript
Available Online from 04 March 2024
  • Receive Date: 21 May 2023
  • Revise Date: 15 December 2023
  • Accept Date: 04 March 2024