Switch to: References

Citations of:

Coherence, Belief Expansion and Bayesian Networks

In BaralC (ed.), Proceedings of the 8th International Workshop on Non-Monotonic Reasoning, NMR'2000 (2000)

Add citations

You must login to add citations.
  1. A dynamic interaction between machine learning and the philosophy of science.Jon Williamson - 2004 - Minds and Machines 14 (4):539-549.
    The relationship between machine learning and the philosophy of science can be classed as a dynamic interaction: a mutually beneficial connection between two autonomous fields that changes direction over time. I discuss the nature of this interaction and give a case study highlighting interactions between research on Bayesian networks in machine learning and research on causality and probability in the philosophy of science.
    Download  
     
    Export citation  
     
    Bookmark   4 citations  
  • On Correspondence.Stephan Hartmann - 2002 - Studies in History and Philosophy of Science Part B: Studies in History and Philosophy of Modern Physics 33 (1):79-94.
    This paper is an essay review of Steven French and Harmke Kamminga (eds.), Correspondence, Invariance and Heuristics. Essays in Honour of Heinz Post (Dordrecht: Kluwer, 1993). I distinguish a varity of correspondence relations between scientific theories (exemplified by cases from the book under review) and examine how one can make sense of the the prevailing continuity in scientific theorizing.
    Download  
     
    Export citation  
     
    Bookmark   17 citations  
  • Effective Field Theories, Reductionism and Scientific Explanation.Stephan Hartmann - 2001 - Studies in History and Philosophy of Science Part B: Studies in History and Philosophy of Modern Physics 32 (2):267-304.
    Effective field theories have been a very popular tool in quantum physics for almost two decades. And there are good reasons for this. I will argue that effective field theories share many of the advantages of both fundamental theories and phenomenological models, while avoiding their respective shortcomings. They are, for example, flexible enough to cover a wide range of phenomena, and concrete enough to provide a detailed story of the specific mechanisms at work at a given energy scale. So will (...)
    Download  
     
    Export citation  
     
    Bookmark   47 citations  
  • (2 other versions)Belief Expansion, Contextual Fit and the Reliability of Information Sources.Stephan Hartmann & L. Bovens - 2001 - In Varol Akman, Paolo Bouquet, Richmond Thomason & Roger A. Young (eds.), Modeling and Using Context, volume 2116 of. Springer-Verlag. pp. 421-424.
    We develop a probabilistic criterion for belief expansion that is sensitive to the degree of contextual fit of the new information to our belief set as well as to the reliability of our information source. We contrast our approach with the success postulate in AGM-style belief revision and show how the idealizations in our approach can be relaxed by invoking Bayesian-Network models.
    Download  
     
    Export citation  
     
    Bookmark   1 citation  
  • Believing more, risking less: On coherence, truth and non-trivial extensions.Luc Bovens & Erik J. Olsson - 2002 - Erkenntnis 57 (2):137 - 150.
    If you believe more things you thereby run a greater risk of being in error than if you believe fewer things. From the point of view of avoiding error, it is best not to believe anything at all, or to have very uncommitted beliefs. But considering the fact that we all in fact do entertain many specific beliefs, this recommendation is obviously in flagrant dissonance with our actual epistemic practice. Let us call the problem raised by this apparent conflict the (...)
    Download  
     
    Export citation  
     
    Bookmark   25 citations  
  • Robustness analysis versus reliable process reasoning: Robert Hudson: Seeing things: The philosophy of reliable observation. Oxford: Oxford University Press, 2014, xii+274pp, £41.99, $58.50 HB.Chiara Lisciandra - 2014 - Metascience 24 (1):37-41.
    Robert Hudson’s book is a contribution to the recent debate on robustness analysis in scientific practice, with a specific focus on the empirical sciences. In this context, robustness analysis is defined as a way to increase the probability of a certain hypothesis by showing that the same result is obtained from several, alternative methods. The rationale underlying this practice is that it would be highly unlikely if different, independent means of observation provided the same wrong outcome.We do not believe in (...)
    Download  
     
    Export citation  
     
    Bookmark