97,830 research outputs found
Bayesian Discovery of Multiple Bayesian Networks via Transfer Learning
Bayesian network structure learning algorithms with limited data are being
used in domains such as systems biology and neuroscience to gain insight into
the underlying processes that produce observed data. Learning reliable networks
from limited data is difficult, therefore transfer learning can improve the
robustness of learned networks by leveraging data from related tasks. Existing
transfer learning algorithms for Bayesian network structure learning give a
single maximum a posteriori estimate of network models. Yet, many other models
may be equally likely, and so a more informative result is provided by Bayesian
structure discovery. Bayesian structure discovery algorithms estimate posterior
probabilities of structural features, such as edges. We present transfer
learning for Bayesian structure discovery which allows us to explore the shared
and unique structural features among related tasks. Efficient computation
requires that our transfer learning objective factors into local calculations,
which we prove is given by a broad class of transfer biases. Theoretically, we
show the efficiency of our approach. Empirically, we show that compared to
single task learning, transfer learning is better able to positively identify
true edges. We apply the method to whole-brain neuroimaging data.Comment: 10 page
Learning Credal Sum-Product Networks
Probabilistic representations, such as Bayesian and Markov networks, are
fundamental to much of statistical machine learning. Thus, learning
probabilistic representations directly from data is a deep challenge, the main
computational bottleneck being inference that is intractable. Tractable
learning is a powerful new paradigm that attempts to learn distributions that
support efficient probabilistic querying. By leveraging local structure,
representations such as sum-product networks (SPNs) can capture high tree-width
models with many hidden layers, essentially a deep architecture, while still
admitting a range of probabilistic queries to be computable in time polynomial
in the network size. While the progress is impressive, numerous data sources
are incomplete, and in the presence of missing data, structure learning methods
nonetheless revert to single distributions without characterizing the loss in
confidence. In recent work, credal sum-product networks, an imprecise extension
of sum-product networks, were proposed to capture this robustness angle. In
this work, we are interested in how such representations can be learnt and thus
study how the computational machinery underlying tractable learning and
inference can be generalized for imprecise probabilities.Comment: Accepted to AKBC 202
Learning Bayesian networks with local structure, mixed variables, and exact algorithms
Modern exact algorithms for structure learning in Bayesian networks first compute an exact local score of every candidate parent set, and then find a network structure by combinatorial optimization so as to maximize the global score. This approach assumes that each local score can be computed fast, which can be problematic when the scarcity of the data calls for structured local models or when there are both continuous and discrete variables, for these cases have lacked efficient-to-compute local scores. To address this challenge, we introduce a local score that is based on a class of classification and regression trees. We show that under modest restrictions on the possible branchings in the tree structure, it is feasible to find a structure that maximizes a Bayes score in a range of moderate-size problem instances. In particular, this enables global optimization of the Bayesian network structure, including the local structure. In addition, we introduce a related model class that extends ordinary conditional probability tables to continuous variables by employing an adaptive discretization approach. The two model classes are compared empirically by learning Bayesian networks from benchmark real-world and synthetic data sets. We discuss the relative strengths of the model classes in terms of their structure learning capability, predictive performance, and running time. (C) 2019 The Authors. Published by Elsevier Inc.Peer reviewe
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