837 research outputs found

    Use of Scientific Experiment Data in Preliminary Design of a Post-Skylab Space Station

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    A computer simulation model with accompanying input and analysis techniques has been developed which will generate Phase A spacecraft preliminary design data using a minimum of computer time, allowing maximum flexibility, and requiring a minimum of learning effort by the user. The application of this model to Space Station design, the construction of a data base for earth orbit experiments, and the Candidate Experiment Program for Manned Space Stations (Blue Book) are dis- cussed.The Blue Book was the primary reference for experiment data, and its contents, organization and current status are described

    Determinants of Long-Term Growth: A Bayesian Averaging of Classical Estimates (BACE) Approach

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    This paper examines the robustness of explanatory variables in cross-country economic growth regressions. It employs a novel approach, Bayesian Averaging of Classical Estimates (BACE), which constructs estimates as a weighted average of OLS estimates for every possible combination of included variables. The weights applied to individual regressions are justified on Bayesian grounds in a way similar to the well-known Schwarz criterion. Of 32 explanatory variables we find 11 to be robustly partially correlated with long-term growth and another five variables to be marginally related. Of all the variables considered, the strongest evidence is for the initial level of real GDP per capita.

    Almost Everybody Disagrees Almost All the Time: The Genericity of Weakly-Merging Nowhere

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    Suppose we randomly pull two agents from a population and ask them to observe an unfolding, infinite sequence of zeros and ones. If each agent starts with a prior belief about the true sequence and updates this belief on revelation of successive observations, what is the chance that the two agents will come to agree on the likelihood that the next draw is a one? In this paper we show that there is no chance. More formally, we show that under a very unrestrictive definition of what it means to draw priors "randomly," the probability that two priors have any chance of weakly merging is zero. Indeed, almost surely, the two measures will be singular—one prior will think certain to occur a set of sequences that the other thinks impossible, and vice versa. Our result is meant as a critique of the "rational learning" literature, which seeks positive convergence results on infinite product spaces by augmenting the process of Bayesian updating with seeming regularity conditions, variously labeled "consistency" or "compatibility" assumptions. Our object is to investigate just how regular these assumption and results are when considered in the space of all possible prior distributions. Our results on the genericity of nowhere weak merging and singularity speak not just to the specific assumptions and results that appear in the literature, but to the "rational learning" approach generally. We call instead for a different approach to learning, one that recognizes the necessity of genuine, substantive restrictions on beliefs and proposes "extra rational" restrictions that are explicitly grounded in our best understanding of human behavior, ideally gleaned from experimental data

    The Limits to Land Reform: The Land Acts in Ireland, 1870-1909

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    Minimum Separation for Single-Layer Channel Routing

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    We present a linear-time algorithm for determining the minimum height of a single-layer routing channel. The algorithm handles single-sided connections and multiterminal nets. It yields a simple routability test for single-layer switchboxes, correcting an error in the literature

    Pitch angle scattering of cometary ions into monospherical and bispherical distributions

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    Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/95043/1/grl5478.pd
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