6,623 research outputs found
Exploiting Causal Independence in Bayesian Network Inference
A new method is proposed for exploiting causal independencies in exact
Bayesian network inference. A Bayesian network can be viewed as representing a
factorization of a joint probability into the multiplication of a set of
conditional probabilities. We present a notion of causal independence that
enables one to further factorize the conditional probabilities into a
combination of even smaller factors and consequently obtain a finer-grain
factorization of the joint probability. The new formulation of causal
independence lets us specify the conditional probability of a variable given
its parents in terms of an associative and commutative operator, such as
``or'', ``sum'' or ``max'', on the contribution of each parent. We start with a
simple algorithm VE for Bayesian network inference that, given evidence and a
query variable, uses the factorization to find the posterior distribution of
the query. We show how this algorithm can be extended to exploit causal
independence. Empirical studies, based on the CPCS networks for medical
diagnosis, show that this method is more efficient than previous methods and
allows for inference in larger networks than previous algorithms.Comment: See http://www.jair.org/ for any accompanying file
Structural Agnostic Modeling: Adversarial Learning of Causal Graphs
A new causal discovery method, Structural Agnostic Modeling (SAM), is
presented in this paper. Leveraging both conditional independencies and
distributional asymmetries in the data, SAM aims at recovering full causal
models from continuous observational data along a multivariate non-parametric
setting. The approach is based on a game between players estimating each
variable distribution conditionally to the others as a neural net, and an
adversary aimed at discriminating the overall joint conditional distribution,
and that of the original data. An original learning criterion combining
distribution estimation, sparsity and acyclicity constraints is used to enforce
the end-to-end optimization of the graph structure and parameters through
stochastic gradient descent. Besides the theoretical analysis of the approach
in the large sample limit, SAM is extensively experimentally validated on
synthetic and real data
Learning Large-Scale Bayesian Networks with the sparsebn Package
Learning graphical models from data is an important problem with wide
applications, ranging from genomics to the social sciences. Nowadays datasets
often have upwards of thousands---sometimes tens or hundreds of thousands---of
variables and far fewer samples. To meet this challenge, we have developed a
new R package called sparsebn for learning the structure of large, sparse
graphical models with a focus on Bayesian networks. While there are many
existing software packages for this task, this package focuses on the unique
setting of learning large networks from high-dimensional data, possibly with
interventions. As such, the methods provided place a premium on scalability and
consistency in a high-dimensional setting. Furthermore, in the presence of
interventions, the methods implemented here achieve the goal of learning a
causal network from data. Additionally, the sparsebn package is fully
compatible with existing software packages for network analysis.Comment: To appear in the Journal of Statistical Software, 39 pages, 7 figure
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