Identifying compelled edges is important in learning the structure (i.e., the DAG) of a Bayesian network. A graphical method (Chickering 1995) was proposed to solve this problem. In this paper, we show that a joint probability distribution defined by a Bayesian network can be uniquely characterized by its intrinsic factorization. Based on such an algebraic characterization, we suggest a simple ...

composting as one of the municipal solid waste management strategies aims to reduce size and weight of excreted substances, to abate odor and leachate, increase resource recovery and reduce the cost of disposal. environmental impact assessment (eia) of compost plants is required for compliance with laws and regulations. eia is one of the effective methods to protect environment. the aim of this...

This paper discusses issues related to Bayesian network model learning for unbalanced binary classification tasks. In general, the primary focus of current research on Bayesian network learning systems (e.g., K2 and its variants) is on the creation of the Bayesian network structure that fits the database best. It turns out that when applied with a specific purpose in mind, such as classificatio...

Continuous time Bayesian network classifiers are designed for temporal classification of multivariate streaming data when time duration of events matters and the class does not change over time. This paper introduces the CTBNCToolkit: an open source Java toolkit which provides a stand-alone application for temporal classification and a library for continuous time Bayesian network classifiers. C...

Over the past several years Bayesian net­ works have been applied to a wide variety of problems. A central problem in applying Bayesian networks is that of finding one or more of the most probable instantiations of a network. In this paper we develop an efficient algorithm that incrementally enumerates the instantiations of a Bayesian network in de­ creasing order of probability. Such enumer­ a...

This paper addresses issues associated with the real-time control of public transit operations to minimize passenger wait time: namely vehicle headway, maintenance of passenger comfort, and reducing the impact of control strategies. The randomness of passenger arrivals at bus stops and external factors (such as traffic congestion and bad weather) in high frequency transit operations often cause...

Over the past several years Bayesian net works have been applied to a wide variety of problems A central problem in applying Bayesian networks is that of nding one or more of the most probable instantiations of a network In this paper we develop an e cient algorithm that incrementally enumerates the instantiations of a Bayesian network in de creasing order of probability Such enumer ation algor...

We present a new method to approximate posterior probabilities of Bayesian Network using Deep Neural Network. Experiment results on several public Bayesian Network datasets shows that Deep Neural Network is capable of learning joint probability distribution of Bayesian Network by learning from a few observation and posterior probability distribution pairs with high accuracy. Compared with tradi...

Bayesian network is a complete model for the variables and their relationships, it can be used to answer probabilistic queries about them. A Bayesian network can thus be considered a mechanism for automatically applying Bayes’ theorem to complex problems. In the application of Bayesian networks, most of the work is related to probabilistic inferences. Any variable updating in any node of Bayesi...