A Polynomial-Time Algorithm for Deciding Markov Equivalence of Directed Cyclic Graphical Models

نویسنده

  • Thomas S. Richardson
چکیده

Although the concept of d-separation was originally defined for directed acyclic graphs (see Pearl 1988), there is a natural extension of the concept to directed cyclic graphs. When exactly the same set of d-separation relations hold in two directed graphs, no matter whether respectively cyclic or acyclic, we say that they are Markov equivalent. In other words, when two directed cyclic graphs are Markov equivalent, the set of distributions that satisfy a natural extension of the Global Directed Markov Condition (Lauritzen et al. 1990) is exactly the same for each graph. There is an obvious exponential (in the number of vertices) time algorithm for deciding Markov equivalence of two directed cyclic graphs; simply check all of the d-separation relations in each graph. In this paper I state a theorem that gives necessary and sufficient conditions for the Markov equivalence of two directed cyclic graphs, where each of the conditions can be checked in polynomial time. Hence, the theorem can be easily adapted into a polynomial time algorithm for deciding the Markov equivalence of two directed cyclic graphs. Although space prohibits inclusion of correctness proofs, they are fully described in Richardson (1994b).

برای دانلود رایگان متن کامل این مقاله و بیش از 32 میلیون مقاله دیگر ابتدا ثبت نام کنید

ثبت نام

اگر عضو سایت هستید لطفا وارد حساب کاربری خود شوید

منابع مشابه

A polynomial algorithm for deciding equivalence in directed cyclic graphical models

NOTICE WARNING CONCERNING COPYRIGHT RESTRICTIONS: The copyright law of the United States (title 17, U.S. Code) governs the making of photocopies or other reproductions of copyrighted material. Any copying of this document without permission of its author may be prohibited by law. 1 Introduction: This paper is concerned with statistical dependence and independence in linear causal systems with f...

متن کامل

Sequences of regressions and their independences

Ordered sequences of univariate or multivariate regressions provide statistical models for analysing data from randomized, possibly sequential interventions, from cohort or multi-wave panel studies, but also from cross-sectional or retrospective studies. Conditional independences are captured by what we name regression graphs, provided the generated distribution shares some properties with a jo...

متن کامل

Learning Bayesian Network Structure using Markov Blanket in K2 Algorithm

‎A Bayesian network is a graphical model that represents a set of random variables and their causal relationship via a Directed Acyclic Graph (DAG)‎. ‎There are basically two methods used for learning Bayesian network‎: ‎parameter-learning and structure-learning‎. ‎One of the most effective structure-learning methods is K2 algorithm‎. ‎Because the performance of the K2 algorithm depends on node...

متن کامل

Discovering cyclic causal structure

This paper is concerned with the problem of making causal inferences from observational data, when the underlying causal structure may involve feedback loops. In particular, making causal inferences under the assumption that the causal system which generated the data is linear and that there are no unmeasured common causes (latent variables). Linear causal structures of this type can be represe...

متن کامل

Identifiability assumptions for directed graphical models with feedback

Directed graphical models provide a useful framework for modeling causal or directional relationships for multivariate data. Prior work has largely focused on identifiability and search algorithms for directed acyclic graphical (DAG) models. In many applications, feedback naturally arises and directed graphical models that permit cycles arise. However theory and methodology 15 for directed grap...

متن کامل

ذخیره در منابع من


  با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید

عنوان ژورنال:
  • CoRR

دوره abs/1302.3600  شماره 

صفحات  -

تاریخ انتشار 2011