35,665 research outputs found
Commentary on Alternative Strategies for Identifying High-Performing Charter Schools in Texas
In the last few years policy makers and practitioners nationally have shown much interest in identifying, recognizing, and replicating successful charter schools, many of which are showing that they can educate low-income and otherwise at-risk students remarkably well. However past efforts to identify high performing schools have been problematic. Using these systematic, rigorous value-added methods, the authors identify 44 Open Enrollment charter schools that merit a “high-performer” rating. Nearly all of those campuses identified serve a disadvantaged student population. The article also finds that most of those high performers are highly cost-effective, earning high ratings on the cost-efficiency measures. The authors argue for more widespread use of value-added modeling in the state accountability system. The approach taken to identifying high-performers is sensible and fair, but any formulaic approach to school labels comes with some limitations
Model selection and local geometry
We consider problems in model selection caused by the geometry of models
close to their points of intersection. In some cases---including common classes
of causal or graphical models, as well as time series models---distinct models
may nevertheless have identical tangent spaces. This has two immediate
consequences: first, in order to obtain constant power to reject one model in
favour of another we need local alternative hypotheses that decrease to the
null at a slower rate than the usual parametric (typically we will
require or slower); in other words, to distinguish between the
models we need large effect sizes or very large sample sizes. Second, we show
that under even weaker conditions on their tangent cones, models in these
classes cannot be made simultaneously convex by a reparameterization.
This shows that Bayesian network models, amongst others, cannot be learned
directly with a convex method similar to the graphical lasso. However, we are
able to use our results to suggest methods for model selection that learn the
tangent space directly, rather than the model itself. In particular, we give a
generic algorithm for learning Bayesian network models
Graphical methods for inequality constraints in marginalized DAGs
We present a graphical approach to deriving inequality constraints for
directed acyclic graph (DAG) models, where some variables are unobserved. In
particular we show that the observed distribution of a discrete model is always
restricted if any two observed variables are neither adjacent in the graph, nor
share a latent parent; this generalizes the well known instrumental inequality.
The method also provides inequalities on interventional distributions, which
can be used to bound causal effects. All these constraints are characterized in
terms of a new graphical separation criterion, providing an easy and intuitive
method for their derivation.Comment: A final version will appear in the proceedings of the 22nd Workshop
on Machine Learning and Signal Processing, 201
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The full-spectrum correlated-k method for longwave atmospheric radiative transfer using an effective Planck function
The correlated k-distribution (CKD) method is widely used in the radiative transfer schemes of atmospheric models and involves dividing the spectrum into a number of bands and then reordering the gaseous absorption coefficients within each one. The fluxes and heating rates for each band may then be computed by discretizing the reordered spectrum into of order 10 quadrature points per major gas and performing a monochromatic radiation calculation for each point. In this presentation it is shown that for clear-sky longwave calculations, sufficient accuracy for most applications can be achieved without the need for bands: reordering may be performed on the entire longwave spectrum. The resulting full-spectrum correlated k (FSCK) method requires significantly fewer monochromatic calculations than standard CKD to achieve a given accuracy. The concept is first demonstrated by comparing with line-by-line calculations for an atmosphere containing only water vapor, in which it is shown that the accuracy of heating-rate calculations improves approximately in proportion to the square of the number of quadrature points. For more than around 20 points, the root-mean-squared error flattens out at around 0.015 K/day due to the imperfect rank correlation of absorption spectra at different pressures in the profile. The spectral overlap of m different gases is treated by considering an m-dimensional hypercube where each axis corresponds to the reordered spectrum of one of the gases. This hypercube is then divided up into a number of volumes, each approximated by a single quadrature point, such that the total number of quadrature points is slightly fewer than the sum of the number that would be required to treat each of the gases separately. The gaseous absorptions for each quadrature point are optimized such that they minimize a cost function expressing the deviation of the heating rates and fluxes calculated by the FSCK method from line-by-line calculations for a number of training profiles. This approach is validated for atmospheres containing water vapor, carbon dioxide, and ozone, in which it is found that in the troposphere and most of the stratosphere, heating-rate errors of less than 0.2 K/day can be achieved using a total of 23 quadrature points, decreasing to less than 0.1 K/day for 32 quadrature points. It would be relatively straightforward to extend the method to include other gases
Analytic and topological index maps with values in the K-theory of mapping cones
Index maps taking values in the -theory of a mapping cone are defined and
discussed. The resulting index theorem can be viewed in analogy with the
Freed-Melrose index theorem. The framework of geometric -homology is used in
a fundamental way. In particular, an explicit isomorphism from a geometric
model for -homology with coefficients in a mapping cone, , to
is constructed.Comment: 22 page
Graphs for margins of Bayesian networks
Directed acyclic graph (DAG) models, also called Bayesian networks, impose
conditional independence constraints on a multivariate probability
distribution, and are widely used in probabilistic reasoning, machine learning
and causal inference. If latent variables are included in such a model, then
the set of possible marginal distributions over the remaining (observed)
variables is generally complex, and not represented by any DAG. Larger classes
of mixed graphical models, which use multiple edge types, have been introduced
to overcome this; however, these classes do not represent all the models which
can arise as margins of DAGs. In this paper we show that this is because
ordinary mixed graphs are fundamentally insufficiently rich to capture the
variety of marginal models.
We introduce a new class of hyper-graphs, called mDAGs, and a latent
projection operation to obtain an mDAG from the margin of a DAG. We show that
each distinct marginal of a DAG model is represented by at least one mDAG, and
provide graphical results towards characterizing when two such marginal models
are the same. Finally we show that mDAGs correctly capture the marginal
structure of causally-interpreted DAGs under interventions on the observed
variables
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