Learning sparse DAG models based on continuous type data = 연속형 자료 기반 희소 DAG 모형 학습

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The graphical models are used to represent the conditional independence relationship of the random variables. Especially, the graphical model whose model structure is given in the form of a DAG(directed acyclic graph) is useful for expressing causal relationships between random variables. We can read data more easily and efficiently by learning the model structure provided that the data are from a DAG model. In this paper, we propose a new method of learning a DAG structure for given continuous type data under condition that the structure is sparse. We checked that the proposed method works well and fast in finding a nearly optimal model in many situations. To carry out this, we begin with an undirected graph which is constructed by applying a nonparametric regression method such as the random forest method. We then assign directions to the edges in such a way that the likelihood of the model may increase at each assignment of edge-direction. It is imperative that directed cycles are to be avoided in the DAG. In case of sparse DAG models, $L_1$ penalized log-likelihood would also be instrumental for the DAG learning.
Advisors
Kim, Sung-Horesearcher김성호researcher
Description
한국과학기술원 : 수리과학과,
Publisher
한국과학기술원
Issue Date
2011
Identifier
467729/325007  / 020093454
Language
eng
Description

학위논문(석사) - 한국과학기술원 : 수리과학과, 2011.2, [ iv, 19 p. ]

Keywords

Random forests; DAG; Bayesian network; Graphical model; Structure learning; 구조 학습; 랜덤 포레스트; DAG; 베이지안 네트워크; 그래프 모형

URI
http://hdl.handle.net/10203/42249
Link
http://library.kaist.ac.kr/search/detail/view.do?bibCtrlNo=467729&flag=dissertation
Appears in Collection
MA-Theses_Master(석사논문)
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