27 resultados para Richardson, John, 1667-1753.
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http://www.archive.org/details/fortyyearsamongt00craiuoft
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http://www.archive.org/details/historyofcatholi00sheaiala
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http://www.archive.org/details/jamesevans00maclrich
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http://name.umdl.umich.edu/ABB4262
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http://www.archive.org/details/womeninthemissio00telfuoft
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http://www.archive.org/details/johnwesleytheman00pikeuoft
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http://www.archive.org/details/missionarypionee00stewrich
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University of California Libraries
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http://www.archive.org/details/75yearsmadurami00chanuoft
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http://www.archive.org/details/bibleillustratio00ingluoft
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Throughout the history of the Church, the Epistle to the Hebrews has been one of the most puzzling letters in the Canon, particularly regarding the implications of understanding the person of Jesus Christ. John Chrysostom, an important patristic writer, is acknowledged to have made significant contributions to the exegesis of this letter. Chrysostom's thought became the norm for traditional thinking and interpretation of this letter in the Middle Ages. Martin Luther's reception of Chrysostom's Homilies on Hebrews presents a unique interpretation that some scholars may describe as the "Reformation Discovery" on Hebrews. In tracing Luther's reception and appropriation of Chrysostom's exegesis of the letter to the Hebrews, there is a noticeable and significant shift in Christological interpretation. Whether or not these modifications were necessary is a matter of debate; however, they do reflect Luther's contextual and existential questions regarding faith, Christ and knowledge of God, which is evident in his Lectures on Hebrews.
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Considerable attention has been focused on the properties of graphs derived from Internet measurements. Router-level topologies collected via traceroute studies have led some authors to conclude that the router graph of the Internet is a scale-free graph, or more generally a power-law random graph. In such a graph, the degree distribution of nodes follows a distribution with a power-law tail. In this paper we argue that the evidence to date for this conclusion is at best insufficient. We show that graphs appearing to have power-law degree distributions can arise surprisingly easily, when sampling graphs whose true degree distribution is not at all like a power-law. For example, given a classical Erdös-Rényi sparse, random graph, the subgraph formed by a collection of shortest paths from a small set of random sources to a larger set of random destinations can easily appear to show a degree distribution remarkably like a power-law. We explore the reasons for how this effect arises, and show that in such a setting, edges are sampled in a highly biased manner. This insight allows us to distinguish measurements taken from the Erdös-Rényi graphs from those taken from power-law random graphs. When we apply this distinction to a number of well-known datasets, we find that the evidence for sampling bias in these datasets is strong.