Learning Equivalence Classes of Bayesian Network Structures

نویسنده

  • David Maxwell Chickering
چکیده

Two Bayesian-network structures are said to be equivalent if the set of distributions that can be represented with one of those structures is identical to the set of distributions that can be represented with the other. Many scoring criteria that are used to learn Bayesiannetwork structures from data are score equivalent; that is, these criteria do not distinguish among networks that are equivalent. In this paper, we consider using a score equivalent criterion in conjunction with a heuristic search algorithm to perform model selection or model averaging. We argue that it is often appropriate to search among equivalence classes of network structures as opposed to the more common approach of searching among individual Bayesian-network structures. We describe a convenient graphical representation for an equivalence class of structures, and introduce a set of operators that can be applied to that representation by a search algorithm to move among equivalence classes. We show that our equivalence-class operators can be scored locally, and thus share the computational efficiency of traditional operators defined for individual structures. We show experimentally that a greedy model-selection algorithm using our representation yields slightly higherscoring structures than the traditional approach without any additional time overhead, and we argue that more sophisticated search algorithms are likely to benefit much more.

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

Speeding up the learning of equivalence classes of bayesian network structures

For some time, learning Bayesian networks has been both feasible and useful in many problems domains. Recently research has been done on learning equivalence classes of Bayesian networks, i.e. structures that capture all of the graphical information of a group of Bayesian networks, in order to increase learning speed and quality. However learning speed still remains quite slow, especially on pr...

متن کامل

Methods to Accelerate the Learning of Bayesian Network Structures

Bayesian networks have become a standard technique in the representation of uncertain knowledge. This paper proposes methods that can accelerate the learning of a Bayesian network structure from a data set. These methods are applicable when learning an equivalence class of Bayesian network structures whilst using a score and search strategy. They work by constraining the number of validity test...

متن کامل

Learning Equivalence Classes of Bayesian Networks Structures

Approaches to learning Bayesian networks from data typically combine a scoring func­ tion with a heuristic search procedure. Given a Bayesian network structure, many of the scoring functions derived in the literature re­ turn a score for the entire equivalence class to which the structure belongs. When using such a scoring function, it is appropriate for the heuristic search algorithm to search...

متن کامل

Enumerating Equivalence Classes of Bayesian Networks using EC Graphs

We consider the problem of learning Bayesian network structures from complete data. In particular, we consider the enumeration of their k-best equivalence classes. We propose a new search space for A* search, called the EC graph, that facilitates the enumeration of equivalence classes, by representing the space of completed, partially directed acyclic graphs. We also propose a canonization of t...

متن کامل

Finding the k-best Equivalence Classes of Bayesian Network Structures for Model Averaging

We develop an algorithm to find the k-best equivalence classes of Bayesian networks. Our algorithm is capable of finding much more best DAGs than the previous algorithm that directly finds the k-best DAGs [1]. We demonstrate our algorithm in the task of Bayesian model averaging. Empirical results show that our algorithm significantly outperforms the k-best DAG algorithm in both time and space t...

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:
  • Journal of Machine Learning Research

دوره 2  شماره 

صفحات  -

تاریخ انتشار 1996