1,228 research outputs found
Hierarchical Models for Independence Structures of Networks
We introduce a new family of network models, called hierarchical network
models, that allow us to represent in an explicit manner the stochastic
dependence among the dyads (random ties) of the network. In particular, each
member of this family can be associated with a graphical model defining
conditional independence clauses among the dyads of the network, called the
dependency graph. Every network model with dyadic independence assumption can
be generalized to construct members of this new family. Using this new
framework, we generalize the Erd\"os-R\'enyi and beta-models to create
hierarchical Erd\"os-R\'enyi and beta-models. We describe various methods for
parameter estimation as well as simulation studies for models with sparse
dependency graphs.Comment: 19 pages, 7 figure
Bayesian optimization of the PC algorithm for learning Gaussian Bayesian networks
The PC algorithm is a popular method for learning the structure of Gaussian
Bayesian networks. It carries out statistical tests to determine absent edges
in the network. It is hence governed by two parameters: (i) The type of test,
and (ii) its significance level. These parameters are usually set to values
recommended by an expert. Nevertheless, such an approach can suffer from human
bias, leading to suboptimal reconstruction results. In this paper we consider a
more principled approach for choosing these parameters in an automatic way. For
this we optimize a reconstruction score evaluated on a set of different
Gaussian Bayesian networks. This objective is expensive to evaluate and lacks a
closed-form expression, which means that Bayesian optimization (BO) is a
natural choice. BO methods use a model to guide the search and are hence able
to exploit smoothness properties of the objective surface. We show that the
parameters found by a BO method outperform those found by a random search
strategy and the expert recommendation. Importantly, we have found that an
often overlooked statistical test provides the best over-all reconstruction
results
Localizing the Latent Structure Canonical Uncertainty: Entropy Profiles for Hidden Markov Models
This report addresses state inference for hidden Markov models. These models
rely on unobserved states, which often have a meaningful interpretation. This
makes it necessary to develop diagnostic tools for quantification of state
uncertainty. The entropy of the state sequence that explains an observed
sequence for a given hidden Markov chain model can be considered as the
canonical measure of state sequence uncertainty. This canonical measure of
state sequence uncertainty is not reflected by the classic multivariate state
profiles computed by the smoothing algorithm, which summarizes the possible
state sequences. Here, we introduce a new type of profiles which have the
following properties: (i) these profiles of conditional entropies are a
decomposition of the canonical measure of state sequence uncertainty along the
sequence and makes it possible to localize this uncertainty, (ii) these
profiles are univariate and thus remain easily interpretable on tree
structures. We show how to extend the smoothing algorithms for hidden Markov
chain and tree models to compute these entropy profiles efficiently.Comment: Submitted to Journal of Machine Learning Research; No RR-7896 (2012
Bayesian Networks for Max-linear Models
We study Bayesian networks based on max-linear structural equations as
introduced in Gissibl and Kl\"uppelberg [16] and provide a summary of their
independence properties. In particular we emphasize that distributions for such
networks are generally not faithful to the independence model determined by
their associated directed acyclic graph. In addition, we consider some of the
basic issues of estimation and discuss generalized maximum likelihood
estimation of the coefficients, using the concept of a generalized likelihood
ratio for non-dominated families as introduced by Kiefer and Wolfowitz [21].
Finally we argue that the structure of a minimal network asymptotically can be
identified completely from observational data.Comment: 18 page
A Bayesian Approach to Inverse Quantum Statistics
A nonparametric Bayesian approach is developed to determine quantum
potentials from empirical data for quantum systems at finite temperature. The
approach combines the likelihood model of quantum mechanics with a priori
information over potentials implemented in form of stochastic processes. Its
specific advantages are the possibilities to deal with heterogeneous data and
to express a priori information explicitly, i.e., directly in terms of the
potential of interest. A numerical solution in maximum a posteriori
approximation was feasible for one--dimensional problems. Using correct a
priori information turned out to be essential.Comment: 4 pages, 6 figures, revte
A Neural Circuit Arbitrates between Persistence and Withdrawal in Hungry Drosophila
In pursuit of food, hungry animals mobilize significant energy resources and overcome exhaustion and fear. How need and motivation control the decision to continue or change behavior is not understood. Using a single fly treadmill, we show that hungry flies persistently track a food odor and increase their effort over repeated trials in the absence of reward suggesting that need dominates negative experience. We further show that odor tracking is regulated by two mushroom body output neurons (MBONs) connecting the MB to the lateral horn. These MBONs, together with dopaminergic neurons and Dop1R2 signaling, control behavioral persistence. Conversely, an octopaminergic neuron, VPM4, which directly innervates one of the MBONs, acts as a brake on odor tracking by connecting feeding and olfaction. Together, our data suggest a function for the MB in internal state-dependent expression of behavior that can be suppressed by external inputs conveying a competing behavioral drive
Microscopic Origin of Quantum Chaos in Rotational Damping
The rotational spectrum of Yb is calculated diagonalizing different
effective interactions within the basis of unperturbed rotational bands
provided by the cranked shell model. A transition between order and chaos
taking place in the energy region between 1 and 2 MeV above the yrast line is
observed, associated with the onset of rotational damping. It can be related to
the higher multipole components of the force acting among the unperturbed
rotational bands.Comment: 7 pages, plain TEX, YITP/K-99
Steinberg modules and Donkin pairs
We prove that in positive characteristic a module with good filtration for a
group of type E6 restricts to a module with good filtration for a subgroup of
type F4. (Recall that a filtration of a module for a semisimple algebraic group
is called good if its layers are dual Weyl modules.) Our result confirms a
conjecture of Brundan for one more case. The method relies on the canonical
Frobenius splittings of Mathieu. Next we settle the remaining cases, in
characteristic not 2, with a computer-aided variation on the old method of
Donkin.Comment: 16 pages; proof of Brundan's conjecture adde
Semiclassical Quantisation Using Diffractive Orbits
Diffraction, in the context of semiclassical mechanics, describes the manner
in which quantum mechanics smooths over discontinuities in the classical
mechanics. An important example is a billiard with sharp corners; its
semiclassical quantisation requires the inclusion of diffractive periodic
orbits in addition to classical periodic orbits. In this paper we construct the
corresponding zeta function and apply it to a scattering problem which has only
diffractive periodic orbits. We find that the resonances are accurately given
by the zeros of the diffractive zeta function.Comment: Revtex document. Submitted to PRL. Figures available on reques
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