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- Yetian Chen, Jin Tian
- AAAI
- 2014

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… (More)

Exact Bayesian structure discovery in Bayesian networks requires exponential time and space. Using dynamic programming (DP), the fastest known serial algorithm computes the exact posterior probabilities of structural features in O(n2 n) time and space, if the number of parents per node or indegree is bounded by a constant d. Here we present a parallel… (More)

—Bayesian network (BN) classifiers with powerful reasoning capabilities have been increasingly utilized to detect intrusion with reasonable accuracy and efficiency. However, existing BN classifiers for intrusion detection suffer two problems. First, such BN classifiers are often trained from data using heuristic methods that usually select suboptimal… (More)

Skill prerequisite information is useful for tutoring systems that assess student knowledge or that provide remediation. These systems often encode prerequisites as graphs designed by subject matter experts in a costly and time-consuming process. In this paper, we introduce Combined student Modeling and prerequisite Discovery (COMMAND), a novel algorithm… (More)

Introduction Bayesian Network (BN) A directed acyclic graph (DAG) where nodes are random variables and directed edges represent probability dependencies among variables BN Structure Learning Firstly construct the topology (structure) of the network Then estimate the parameters (CPDs) given the fixed structure Curriculum Learning (CL) Ideas: learn with the… (More)

Ancestor relations in Bayesian networks (BNs) encode long-range causal relations among random variables. In this paper, we develop dynamic programming (DP) algorithms to compute the exact posterior probabilities of ancestor relations in Bayesian networks. Previous algorithm by Parviainen and Koivisto (2011) evaluates all possible ancestor relations in time… (More)

- Yetian Chen, Jin Tian, Chen
- 2014

In this paper we develop a dynamic programming algorithm to compute the exact posterior probabilities of ancestor relations in Bayesian networks. Previous dynamic programming (DP) algorithm by (Parviainen and Koivisto, 2011) evaluates all possible ancestor relations in time O(n3 n) and space O(3 n). However, their algorithm assumes an order-modular prior… (More)

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