374,843 research outputs found
Learning tractable multidimensional Bayesian network classifiers
Multidimensional classification has become one of the most relevant topics in view of the many
domains that require a vector of class values to be assigned to a vector of given features. The
popularity of multidimensional Bayesian network classifiers has increased in the last few years
due to their expressive power and the existence of methods for learning different families of these
models. The problem with this approach is that the computational cost of using the learned models
is usually high, especially if there are a lot of class variables. Class-bridge decomposability means
that the multidimensional classification problem can be divided into multiple subproblems for these
models. In this paper, we prove that class-bridge decomposability can also be used to guarantee
the tractability of the models. We also propose a strategy for efficiently bounding their inference
complexity, providing a simple learning method with an order-based search that obtains tractable
multidimensional Bayesian network classifiers. Experimental results show that our approach is
competitive with other methods in the state of the art and ensures the tractability of the learned
models
Molecular Network Control Through Boolean Canalization
Boolean networks are an important class of computational models for molecular
interaction networks. Boolean canalization, a type of hierarchical clustering
of the inputs of a Boolean function, has been extensively studied in the
context of network modeling where each layer of canalization adds a degree of
stability in the dynamics of the network. Recently, dynamic network control
approaches have been used for the design of new therapeutic interventions and
for other applications such as stem cell reprogramming. This work studies the
role of canalization in the control of Boolean molecular networks. It provides
a method for identifying the potential edges to control in the wiring diagram
of a network for avoiding undesirable state transitions. The method is based on
identifying appropriate input-output combinations on undesirable transitions
that can be modified using the edges in the wiring diagram of the network.
Moreover, a method for estimating the number of changed transitions in the
state space of the system as a result of an edge deletion in the wiring diagram
is presented. The control methods of this paper were applied to a mutated
cell-cycle model and to a p53-mdm2 model to identify potential control targets
Containing epidemic outbreaks by message-passing techniques
The problem of targeted network immunization can be defined as the one of
finding a subset of nodes in a network to immunize or vaccinate in order to
minimize a tradeoff between the cost of vaccination and the final (stationary)
expected infection under a given epidemic model. Although computing the
expected infection is a hard computational problem, simple and efficient
mean-field approximations have been put forward in the literature in recent
years. The optimization problem can be recast into a constrained one in which
the constraints enforce local mean-field equations describing the average
stationary state of the epidemic process. For a wide class of epidemic models,
including the susceptible-infected-removed and the
susceptible-infected-susceptible models, we define a message-passing approach
to network immunization that allows us to study the statistical properties of
epidemic outbreaks in the presence of immunized nodes as well as to find
(nearly) optimal immunization sets for a given choice of parameters and costs.
The algorithm scales linearly with the size of the graph and it can be made
efficient even on large networks. We compare its performance with topologically
based heuristics, greedy methods, and simulated annealing
Coarse graining methods for spin net and spin foam models
We undertake first steps in making a class of discrete models of quantum
gravity, spin foams, accessible to a large scale analysis by numerical and
computational methods. In particular, we apply Migdal-Kadanoff and Tensor
Network Renormalization schemes to spin net and spin foam models based on
finite Abelian groups and introduce `cutoff models' to probe the fate of gauge
symmetries under various such approximated renormalization group flows. For the
Tensor Network Renormalization analysis, a new Gauss constraint preserving
algorithm is introduced to improve numerical stability and aid physical
interpretation. We also describe the fixed point structure and establish an
equivalence of certain models.Comment: 39 pages, 13 figures, 1 tabl
Maximum likelihood estimation based on the Laplace approximation for p2 network regression models
The class of p2 models is suitable for modeling binary relation data in social network analysis. A p2 model is essentially a regression model for bivariate binary responses, featuring within‐dyad dependence and correlated crossed random effects to represent heterogeneity of actors. Despite some desirable properties, these models are used less frequently in empirical applications than other models for network data. A possible reason for this is due to the limited possibilities for this model for accounting for (and explicitly modeling) structural dependence beyond the dyad as can be done in exponential random graph models. Another motive, however, may lie in the computational difficulties existing to estimate such models by means of the methods proposed in the literature, such as joint maximization methods and Bayesian methods. The aim of this article is to investigate maximum likelihood estimation based on the Laplace approximation approach, that can be refined by importance sampling. Practical implementation of such methods can be performed in an efficient manner, and the article provides details on a software implementation using R. Numerical examples and simulation studies illustrate the methodology
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