Zobrazeno 1 - 10
of 62
pro vyhledávání: '"Chickering, David Maxwell"'
We introduce Selective Greedy Equivalence Search (SGES), a restricted version of Greedy Equivalence Search (GES). SGES retains the asymptotic correctness of GES but, unlike GES, has polynomial performance guarantees. In particular, we show that when
Externí odkaz:
http://arxiv.org/abs/1506.02113
We describe algorithms for learning Bayesian networks from a combination of user knowledge and statistical data. The algorithms have two components: a scoring metric and a search procedure. The scoring metric takes a network structure, statistical da
Externí odkaz:
http://arxiv.org/abs/1302.6815
Autor:
Chickering, David Maxwell
We present a simple characterization of equivalent Bayesian network structures based on local transformations. The significance of the characterization is twofold. First, we are able to easily prove several new invariant properties of theoretical int
Externí odkaz:
http://arxiv.org/abs/1302.4938
We discuss Bayesian methods for learning Bayesian networks when data sets are incomplete. In particular, we examine asymptotic approximations for the marginal likelihood of incomplete data given a Bayesian network. We consider the Laplace approximati
Externí odkaz:
http://arxiv.org/abs/1302.3567
Autor:
Chickering, David Maxwell
Approaches to learning Bayesian networks from data typically combine a scoring function with a heuristic search procedure. Given a Bayesian network structure, many of the scoring functions derived in the literature return a score for the entire equiv
Externí odkaz:
http://arxiv.org/abs/1302.3566
Recently several researchers have investigated techniques for using data to learn Bayesian networks containing compact representations for the conditional probability distributions (CPDs) stored at each node. The majority of this work has concentrate
Externí odkaz:
http://arxiv.org/abs/1302.1528
We describe computationally efficient methods for learning mixtures in which each component is a directed acyclic graphical model (mixtures of DAGs or MDAGs). We argue that simple search-and-score algorithms are infeasible for a variety of problems,
Externí odkaz:
http://arxiv.org/abs/1301.7415
We describe two techniques that significantly improve the running time of several standard machine-learning algorithms when data is sparse. The first technique is an algorithm that effeciently extracts one-way and two-way counts--either real or expec
Externí odkaz:
http://arxiv.org/abs/1301.6685
A simple advertising strategy that can be used to help increase sales of a product is to mail out special offers to selected potential customers. Because there is a cost associated with sending each offer, the optimal mailing strategy depends on both
Externí odkaz:
http://arxiv.org/abs/1301.3842
Autor:
Heckerman, David, Chickering, David Maxwell, Meek, Christopher, Rounthwaite, Robert, Kadie, Carl
We describe a graphical model for probabilistic relationships---an alternative to the Bayesian network---called a dependency network. The graph of a dependency network, unlike a Bayesian network, is potentially cyclic. The probability component of a
Externí odkaz:
http://arxiv.org/abs/1301.3862