942 research outputs found
Bayesian network structure learning with causal effects in the presence of latent variables.
Latent variables may lead to spurious relationships that can be
misinterpreted as causal relationships. In Bayesian Networks (BNs), this
challenge is known as learning under causal insufficiency. Structure learning
algorithms that assume causal insufficiency tend to reconstruct the ancestral
graph of a BN, where bi-directed edges represent confounding and directed edges
represent direct or ancestral relationships. This paper describes a hybrid
structure learning algorithm, called CCHM, which combines the constraint-based
part of cFCI with hill-climbing score-based learning. The score-based process
incorporates Pearl s do-calculus to measure causal effects and orientate edges
that would otherwise remain undirected, under the assumption the BN is a linear
Structure Equation Model where data follow a multivariate Gaussian
distribution. Experiments based on both randomised and well-known networks show
that CCHM improves the state-of-the-art in terms of reconstructing the true
ancestral graph
Tuning structure learning algorithms with out-of-sample and resampling strategies
One of the challenges practitioners face when applying structure learning algorithms to their data involves determining a set of hyperparameters; otherwise, a set of hyperparameter defaults is assumed. The optimal hyperparameter configuration often depends on multiple factors, including the size and density of the usually unknown underlying true graph, the sample size of the input data, and the structure learning algorithm. We propose a novel hyperparameter tuning method, called the Out-of-sample Tuning for Structure Learning (OTSL), that employs out-of-sample and resampling strategies to estimate the optimal hyperparameter configuration for structure learning, given the input dataset and structure learning algorithm. Synthetic experiments show that employing OTSL to tune the hyperparameters of hybrid and score-based structure learning algorithms leads to improvements in graphical accuracy compared to the state-of-the-art. We also illustrate the applicability of this approach to real datasets from different disciplines
Generalized seniority for the shell model with realistic interactions
The generalized seniority scheme has long been proposed as a means of
dramatically reducing the dimensionality of nuclear shell model calculations,
when strong pairing correlations are present. However, systematic benchmark
calculations, comparing results obtained in a model space truncated according
to generalized seniority with those obtained in the full shell model space, are
required to assess the viability of this scheme. Here, a detailed comparison is
carried out, for semimagic nuclei taken in a full major shell and with
realistic interactions. The even-mass and odd-mass Ca isotopes are treated in
the generalized seniority scheme, for generalized seniority v<=3. Results for
level energies, orbital occupations, and electromagnetic observables are
compared with those obtained in the full shell model space.Comment: 13 pages, 8 figures; published in Phys. Rev.
The nucleon spin and momentum decomposition using lattice QCD simulations
We determine within lattice QCD, the nucleon spin carried by valence and sea
quarks, and gluons. The calculation is performed using an ensemble of gauge
configurations with two degenerate light quarks with mass fixed to
approximately reproduce the physical pion mass. We find that the total angular
momentum carried by the quarks in the nucleon is and the gluon contribution is giving a total of consistent with the spin sum. For the quark intrinsic spin contribution
we obtain . All quantities are given in the scheme at
2~GeV. The quark and gluon momentum fractions are also computed and add up to
satisfying the momentum sum.Comment: Version published in PR
Information fusion between knowledge and data in Bayesian network structure learning
Bayesian Networks (BNs) have become a powerful technology for reasoning under
uncertainty, particularly in areas that require causal assumptions that enable
us to simulate the effect of intervention. The graphical structure of these
models can be determined by causal knowledge, learnt from data, or a
combination of both. While it seems plausible that the best approach in
constructing a causal graph involves combining knowledge with machine learning,
this approach remains underused in practice. We implement and evaluate 10
knowledge approaches with application to different case studies and BN
structure learning algorithms available in the open-source Bayesys structure
learning system. The approaches enable us to specify pre-existing knowledge
that can be obtained from heterogeneous sources, to constrain or guide
structure learning. Each approach is assessed in terms of structure learning
effectiveness and efficiency, including graphical accuracy, model fitting,
complexity, and runtime; making this the first paper that provides a
comparative evaluation of a wide range of knowledge approaches for BN structure
learning. Because the value of knowledge depends on what data are available, we
illustrate the results both with limited and big data. While the overall
results show that knowledge becomes less important with big data due to higher
learning accuracy rendering knowledge less important, some of the knowledge
approaches are actually found to be more important with big data. Amongst the
main conclusions is the observation that reduced search space obtained from
knowledge does not always imply reduced computational complexity, perhaps
because the relationships implied by the data and knowledge are in tension
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