2,234 research outputs found
Concepts and a case study for a flexible class of graphical Markov models
With graphical Markov models, one can investigate complex dependences,
summarize some results of statistical analyses with graphs and use these graphs
to understand implications of well-fitting models. The models have a rich
history and form an area that has been intensively studied and developed in
recent years. We give a brief review of the main concepts and describe in more
detail a flexible subclass of models, called traceable regressions. These are
sequences of joint response regressions for which regression graphs permit one
to trace and thereby understand pathways of dependence. We use these methods to
reanalyze and interpret data from a prospective study of child development, now
known as the Mannheim Study of Children at Risk. The two related primary
features concern cognitive and motor development, at the age of 4.5 and 8 years
of a child. Deficits in these features form a sequence of joint responses.
Several possible risks are assessed at birth of the child and when the child
reached age 3 months and 2 years.Comment: 21 pages, 7 figures, 7 tables; invited, refereed chapter in a boo
Bayesian regularization of non-homogeneous dynamic Bayesian networks by globally coupling interaction parameters
To relax the homogeneity assumption of classical dynamic Bayesian networks (DBNs), various recent studies have combined DBNs with multiple changepoint processes. The underlying assumption is that the parameters associated with time series segments delimited by multiple changepoints are a priori independent. Under weak regularity conditions, the parameters can be integrated out in the likelihood, leading to a closed-form expression of the marginal likelihood. However, the assumption of prior independence is unrealistic in many real-world applications, where the segment-specific regulatory relationships among the interdependent quantities tend to undergo gradual evolutionary adaptations. We therefore propose a Bayesian coupling scheme to introduce systematic information sharing among the segment-specific interaction parameters. We investigate the effect this model improvement has on the network reconstruction accuracy in a reverse engineering context, where the objective is to learn the structure of a gene regulatory network from temporal gene expression profiles
Motif Statistics and Spike Correlations in Neuronal Networks
Motifs are patterns of subgraphs of complex networks. We studied the impact
of such patterns of connectivity on the level of correlated, or synchronized,
spiking activity among pairs of cells in a recurrent network model of integrate
and fire neurons. For a range of network architectures, we find that the
pairwise correlation coefficients, averaged across the network, can be closely
approximated using only three statistics of network connectivity. These are the
overall network connection probability and the frequencies of two second-order
motifs: diverging motifs, in which one cell provides input to two others, and
chain motifs, in which two cells are connected via a third intermediary cell.
Specifically, the prevalence of diverging and chain motifs tends to increase
correlation. Our method is based on linear response theory, which enables us to
express spiking statistics using linear algebra, and a resumming technique,
which extrapolates from second order motifs to predict the overall effect of
coupling on network correlation. Our motif-based results seek to isolate the
effect of network architecture perturbatively from a known network state
Coupled Depth Learning
In this paper we propose a method for estimating depth from a single image
using a coarse to fine approach. We argue that modeling the fine depth details
is easier after a coarse depth map has been computed. We express a global
(coarse) depth map of an image as a linear combination of a depth basis learned
from training examples. The depth basis captures spatial and statistical
regularities and reduces the problem of global depth estimation to the task of
predicting the input-specific coefficients in the linear combination. This is
formulated as a regression problem from a holistic representation of the image.
Crucially, the depth basis and the regression function are {\bf coupled} and
jointly optimized by our learning scheme. We demonstrate that this results in a
significant improvement in accuracy compared to direct regression of depth
pixel values or approaches learning the depth basis disjointly from the
regression function. The global depth estimate is then used as a guidance by a
local refinement method that introduces depth details that were not captured at
the global level. Experiments on the NYUv2 and KITTI datasets show that our
method outperforms the existing state-of-the-art at a considerably lower
computational cost for both training and testing.Comment: 10 pages, 3 Figures, 4 Tables with quantitative evaluation
Chain graph models of multivariate regression type for categorical data
We discuss a class of chain graph models for categorical variables defined by
what we call a multivariate regression chain graph Markov property. First, the
set of local independencies of these models is shown to be Markov equivalent to
those of a chain graph model recently defined in the literature. Next we
provide a parametrization based on a sequence of generalized linear models with
a multivariate logistic link function that captures all independence
constraints in any chain graph model of this kind.Comment: Published in at http://dx.doi.org/10.3150/10-BEJ300 the Bernoulli
(http://isi.cbs.nl/bernoulli/) by the International Statistical
Institute/Bernoulli Society (http://isi.cbs.nl/BS/bshome.htm
Climate-Related Floral and Vegetative Size Variation in Leptosiphon androsaceus (Polemoniaceae)
Phenotypic plasticity in plants is a vital adaptive response to changing environmental conditions. Floral and vegetative morphology often varies as biotic and abiotic factors vary. Variation is the basis of evolution, and natural selection, acting on trait variation, can lead to speciation. For this study, floral and vegetative character traits of false babystars (Leptosiphon androsaceus, Polemoniaceae) were analyzed within four populations at Henry W. Coe State Park in 2011. Traits were measured to assess changes in size along a moisture availability gradient. Stable carbon isotope ratio samples were collected to measure integrated water-use efficiency (WUE) as it related to precipitation and floral size. Sizes of floral and vegetative characters varied significantly between the drier and wetter regions. Floral and vegetative character sizes for L. androsaceus decreased from the wetter to the drier region. The ratio of floral area/leaf area increased as precipitation decreased, suggesting that leaves may have incurred a physiological cost from floral growth within the drier region. A difference in WUE, as it related to trait size and precipitation, was not found for this study year. These results provide evidence that floral and vegetative characters of L. androsaceus vary in size in response to water-limiting conditions
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The Ca2+ transient as a feedback sensor controlling cardiomyocyte ionic conductances in mouse populations.
Conductances of ion channels and transporters controlling cardiac excitation may vary in a population of subjects with different cardiac gene expression patterns. However, the amount of variability and its origin are not quantitatively known. We propose a new conceptual approach to predict this variability that consists of finding combinations of conductances generating a normal intracellular Ca2+ transient without any constraint on the action potential. Furthermore, we validate experimentally its predictions using the Hybrid Mouse Diversity Panel, a model system of genetically diverse mouse strains that allows us to quantify inter-subject versus intra-subject variability. The method predicts that conductances of inward Ca2+ and outward K+ currents compensate each other to generate a normal Ca2+ transient in good quantitative agreement with current measurements in ventricular myocytes from hearts of different isogenic strains. Our results suggest that a feedback mechanism sensing the aggregate Ca2+ transient of the heart suffices to regulate ionic conductances
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