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Erich Kummerfeld
Carnegie Mellon University
  1.  47
    Conservatism and the Scientific State of Nature.Erich Kummerfeld & Kevin J. S. Zollman - 2016 - British Journal for the Philosophy of Science 67 (4):1057-1076.
    Those who comment on modern scientific institutions are often quick to praise institutional structures that leave scientists to their own devices. These comments reveal an underlying presumption that scientists do best when left alone—when they operate in what we call the ‘scientific state of nature’. Through computer simulation, we challenge this presumption by illustrating an inefficiency that arises in the scientific state of nature. This inefficiency suggests that one cannot simply presume that science is most efficient when institutional control is (...)
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  2. Opinion: Reproducibility Failures Are Essential to Scientific Inquiry.A. David Redish, Erich Kummerfeld, Rebecca Morris & Alan Love - 2018 - Proceedings of the National Academy of Sciences 115 (20):5042-5046.
    Current fears of a “reproducibility crisis” have led researchers, sources of scientific funding, and the public to question both the efficacy and trustworthiness of science. Suggested policy changes have been focused on statistical problems, such as p-hacking, and issues of experimental design and execution. However, “reproducibility” is a broad concept that includes a number of issues. Furthermore, reproducibility failures occur even in fields such as mathematics or computer science that do not have statistical problems or issues with experimental design. Most (...)
     
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  3.  41
    Model Change and Reliability in Scientific Inference.Erich Kummerfeld & David Danks - 2014 - Synthese 191 (12):2673-2693.
    One persistent challenge in scientific practice is that the structure of the world can be unstable: changes in the broader context can alter which model of a phenomenon is preferred, all without any overt signal. Scientific discovery becomes much harder when we have a moving target, and the resulting incorrect understandings of relationships in the world can have significant real-world and practical consequences. In this paper, we argue that it is common (in certain sciences) to have changes of context that (...)
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  4.  13
    Tracking Time-Varying Graphical Structure.Erich Kummerfeld & David Danks - unknown
    Structure learning algorithms for graphical models have focused almost exclusively on stable environments in which the underlying generative process does not change; that is, they assume that the generating model is globally stationary. In real-world environments, however, such changes often occur without warning or signal. Real-world data often come from generating models that are only locally stationary. In this paper, we present LoSST, a novel, heuristic structure learning algorithm that tracks changes in graphical model structure or parameters in a dynamic, (...)
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  5.  67
    Erratum To: Model Change and Methodological Virtues in Scientific Inference.Erich Kummerfeld & David Danks - 2014 - Synthese 191 (14):3469-3472.
    Erratum to: Synthese DOI 10.1007/s11229-014-0408-3Appendix 1: NotationLet \(X\) represent a sequence of data, and let \(X_B^t\) represent an i.i.d. subsequence of length \(t\) of data generated from distribution \(B\).We conjecture that the i.i.d. assumption could be eliminated by defining probability distributions over sequences of arbitrary length, though this complication would not add conceptual clarity. Let \(\mathbf{F}\) be a framework (in this case, a set of probability distributions or densities).Let any \(P(\,)\) functions be either a probability distribution function or probability density (...)
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