Characterizing the uncertainty in holddown post load measurements

In order to understand unexpectedly erratic load measurements in the launch-pad supports for the space shuttle, the sensitivities of the load cells in the supports were analyzed using simple probabilistic techniques. NASA engineers use the loads in the shuttle's supports to calculate critical stresses in the shuttle vehicle just before lift-off. The support loads are measured with 'load cells' which are actually structural components of the mobile launch platform which have been instrumented with strain gauges. Although these load cells adequately measure vertical loads, the horizontal load measurements have been erratic. The load measurements were simulated in this study using Monte Carlo simulation procedures. The simulation studies showed that the support loads are sensitive to small deviations in strain and calibration. In their current configuration, the load cells will not measure loads with sufficient accuracy to reliably calculate stresses in the shuttle vehicle. A simplified model of the holddown post (HDP) load measurement system was used to study the effect on load measurement accuracy for several factors, including load point deviations, gauge heights, and HDP geometry

Markovian acyclic directed mixed graphs for discrete data

Acyclic directed mixed graphs (ADMGs) are graphs that contain directed ($\rightarrow$) and bidirected ($\leftrightarrow$) edges, subject to the constraint that there are no cycles of directed edges. Such graphs may be used to represent the conditional independence structure induced by a DAG model containing hidden variables on its observed margin. The Markovian model associated with an ADMG is simply the set of distributions obeying the global Markov property, given via a simple path criterion (m-separation). We first present a factorization criterion characterizing the Markovian model that generalizes the well-known recursive factorization for DAGs. For the case of finite discrete random variables, we also provide a parameterization of the model in terms of simple conditional probabilities, and characterize its variation dependence. We show that the induced models are smooth. Consequently, Markovian ADMG models for discrete variables are curved exponential families of distributions.Comment: Published in at http://dx.doi.org/10.1214/14-AOS1206 the Annals of Statistics (http://www.imstat.org/aos/) by the Institute of Mathematical Statistics (http://www.imstat.org

Partitioning of carbon dioxide between the atmosphere and lithosphere on early Mars

It is pointed out that in addition to the 1 to 5 bar CO2 total inventory, a high level of global volcanism was needed to keep the CO2 from being drawn away permanently by weathering of igneous rocks; the volcanism would continually decompose the carbonate resulting in steady efficient recycling

The lower hybrid wave cutoff: A case study in eikonal methods

Eikonal, or ray tracing, methods are commonly used to estimate the propagation of radio frequency fields in plasmas. While the information gained from the rays is quite useful, an approximate solution for the fields would also be valuable, e.g., for comparison to full wave simulations. Such approximations are often difficult to perform numerically because of the special care which must be taken to correctly reconstruct the fields near reflection and focusing caustics. In this paper, we compare the standard eikonal method for approximating fields to a method based on the dynamics of wave packets. We compare the approximations resulting from these two methods to the analytical solution for a lower hybrid wave reflecting from a cutoff. The algorithm based on wave packets has the advantage that it can correctly deal with caustics, without any special treatment.Comment: 12 pages, 17 figures, To appear in Physics of Plasmas, Received 14 December 2009; accepted 29 March 2010

Smooth, identifiable supermodels of discrete DAG models with latent variables

We provide a parameterization of the discrete nested Markov model, which is a supermodel that approximates DAG models (Bayesian network models) with latent variables. Such models are widely used in causal inference and machine learning. We explicitly evaluate their dimension, show that they are curved exponential families of distributions, and fit them to data. The parameterization avoids the irregularities and unidentifiability of latent variable models. The parameters used are all fully identifiable and causally-interpretable quantities.Comment: 30 page

Integrating multiple representations: fighting asthma

This paper seeks to engage debates about integrating pluralisms regarding multiple forms/representations and how they might function smoothly if they are closely aligned. This paper offers, narrative poetry with an artistic impression aimed at seeing how these might interact with each other. Like poetry, visual images are unique and can evoke particular kinds of emotional and visceral responses. By offering narrative poetry together with an artistic representation it is not meant to de-value the importance of either, but it is aimed at seeing how these arts-based methods and creative analytical practices might unite as a narrative to offer knew ways of ‘knowing’ and ‘seeing

Nested Markov Properties for Acyclic Directed Mixed Graphs

Directed acyclic graph (DAG) models may be characterized in at least four different ways: via a factorization, the d-separation criterion, the moralization criterion, and the local Markov property. As pointed out by Robins (1986, 1999), Verma and Pearl (1990), and Tian and Pearl (2002b), marginals of DAG models also imply equality constraints that are not conditional independences. The well-known `Verma constraint' is an example. Constraints of this type were used for testing edges (Shpitser et al., 2009), and an efficient marginalization scheme via variable elimination (Shpitser et al., 2011). We show that equality constraints like the `Verma constraint' can be viewed as conditional independences in kernel objects obtained from joint distributions via a fixing operation that generalizes conditioning and marginalization. We use these constraints to define, via Markov properties and a factorization, a graphical model associated with acyclic directed mixed graphs (ADMGs). We show that marginal distributions of DAG models lie in this model, prove that a characterization of these constraints given in (Tian and Pearl, 2002b) gives an alternative definition of the model, and finally show that the fixing operation we used to define the model can be used to give a particularly simple characterization of identifiable causal effects in hidden variable graphical causal models.Comment: 67 pages (not including appendix and references), 8 figure

A submillimetre wavelength spectral line search of the Orion molecular cloud core

A submillimetre wavelength molecular line search of the Orion molecular cloud has been made covering a total of about 5 percent of the frequency range 342.8 - 358.6 GHz. This search, coupled with the authors' previous observations of submillimetre transitions in this cloud, has led to the detection of 22 transitions of 14 molecular species, of which 16 are reported here for the first time. No unidentified lines have been detected in the present search. Mapping observations have been obtained for several of the lines and, in the case of H2CO the authors have been able to compare the present data with that obtained from other telescopes, to estimate the density and abundance in the emitting region