Skip to main content
SpringerLink
Log in
Book cover

Berechenbarkeit der Welt? pp 129–154Cite as

Scientific Epistemology in the Context of Uncertainty

  • Chapter
  • First Online:

Zusammenfassung

Contemporary science confronts a huge wall: lack of data. Scientists desire to model large-scale systems that are both stochastic and nonlinear, for instance cell regulation; however, relative to the complexity of such systems there is a paucity of data that impedes our ability to both construct and validate models. With regard to large-scale systems and lack of data, this chapter has several aims: (1) discuss the relationship between scientific epistemology grounded in prediction and data insufficiency; (2) discuss the dichotomy between pure science, whose aim is to provide a mathematical representation of Nature, and translational science (engineering), whose aim is the pragmatic application of mathematical modeling to beneficially intervene in Nature, and the manner in which translational science mitigates the data requirement; (3) provide a detailed analysis of the effect of limited data as it applies to pattern recognition; and (4) provide a framework for translational science in the context of model uncertainty, including an epistemologically consequent concept of uncertainty quantification.

Edward R. Dougherty is the Robert M. Kennedy ‘26 Chair and Distinguished Professor in the Department of Electrical and Computer Engineering at Texas A&M University, and is Scientific Director of the Center for Bioinformatics and Genomic Systems Engineering. He holds a Ph.D. in mathematics from Rutgers University and the Doctor Honoris Causa from the Tampere University of Technology.

This is a preview of subscription content, log in via an institution.

Chapter
USD   29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD   64.99
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD   84.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Learn about institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  • Bacon, Francis. 1952 (originally published 1620). Novum Organum. Great Books of the Western World, 35, eds. R. M. Hutchins and M. J. Adler. Chicago: Encyclopedia Britannica.

    Google Scholar 

  • Bohr, Niels. 1958. Atomic Physics and Human Knowledge. New York: John Wiley.

    Google Scholar 

  • Braga-Neto, Ulisses M., and Edward R. Dougherty. 2005. Exact Performance of Error Estimators for Discrete Classifiers. Pattern Recognition. 38: 1799-1814.

    Google Scholar 

  • Braga-Neto, Ulisses M., and Edward R. Dougherty. 2015. Error Estimation for Pattern Recognition. New York: Wiley-IEEE Press.

    Google Scholar 

  • Dalton, Lori A., and Edward R. Dougherty. 2011a. Bayesian Minimum Mean-Square Error Estimation for Classification Error – Part I: Definition and the Bayesian MMSE Error Estimator for Discrete Classification. IEEE Transactions on Signal Processing. 59: 115-129.

    Google Scholar 

  • Dalton, Lori A., and Edwrd R. Dougherty. 2011b. Bayesian Minimum Mean-Square Error Estimation for Classification Error – Part II: Linear Classification of Gaussian Models. IEEE Transactions on Signal Processing. 59: 130-144.

    Google Scholar 

  • Dalton, Lori A., and Edward R. Dougherty. 2012. Exact MSE Performance of the Bayesian MMSE Estimator for Classification Error – Part I: Representation. IEEE Transactions on Signal Processing. 60: 2575-2587.

    Google Scholar 

  • Dalton, Lori A., and Edward R. Dougherty. 2013a. Optimal Classifiers with Minimum Expected Error within a Bayesian Framework – Part I: Discrete and Gaussian Models. Pattern Recognition. 46: 1288-1300.

    Google Scholar 

  • Dalton, Lori A., and Edward R. Dougherty. 2013b. Optimal Classifiers with Minimum Expected Error within a Bayesian Framework – Part II: Properties and Performance Analysis. Pattern Recognition. 46: 1301-1314.

    Google Scholar 

  • Dalton, Lori A., Edward R. Dougherty. 2014. Intrinsically Optimal Bayesian Robust Filtering. IEEE Transactions on Signal Processing. 62: 657-670.

    Google Scholar 

  • Dehghannasiri, Roozbeh, Byung-JunYoon, and Edward R. Dougherty. 2015. Optimal Experimental Design for Gene Regulatory Networks in the Presence of Uncertainty. IEEE/ACM Transactions on Computational Biology and Bioinformatics. 14: 938-950.

    Google Scholar 

  • Devroye, Luc, and T. Wagner. 1979. Distribution-free Inequalities for the Deleted and Holdout Error Estimates. IEEE Transactions on Information Theory. 25: 202-207.

    Google Scholar 

  • Devroye, Luc, Laszlo Gyorfi, and Gabor Lugosi. 1996. A Probabilistic Theory of Pattern Recognition. New York: Springer-Verlag.

    Google Scholar 

  • Dougherty, Edward R. 2009. Translational Science: Epistemology and the Investigative Process. Current Genomics. 10: 102-109.

    Google Scholar 

  • Dougherty, Edward R., and Michael L. Bittner. 2011. Epistemology of the Cell: A Systems Perspective on Biological Knowledge. New York: Wiley-IEEE Press.

    Google Scholar 

  • Dougherty, Edward R., and Ilya Shmulevich. 2012. On the Limitations of Biological Knowledge. Current Genomics. 13: 574-587.

    Google Scholar 

  • Dougherty, Edward R., Jianping Hua, and Michael L. Bittner. 2007. Validation of Computational Methods in Genomics. Current Genomics. 8: 1-19.

    Google Scholar 

  • Dougherty, Edward R., Jianping Hua, Zixiang Xiong, and Yidong Chen. 2005. Optimal Robust Classifiers. Pattern Recognition. 38: 1520-1532.

    Google Scholar 

  • Einstein, Albert. 1944. Remarks on Bertrand Russell’s Theory of Knowledge. In The Philosophy of Bertrand Russell, ed. Paul A. Schlipp. Greensboro: The Library of Living Philosophers, 5.

    Google Scholar 

  • Esfahani, Mohammad S., and Edward R. Dougherty. 2014. Incorporation of Biological Pathway Knowledge in the Construction of Priors for Optimal Bayesian Classification. IEEE/ACM Transactions on Computational Biology and Bioinformatics. 11: 202-218.

    Google Scholar 

  • Grygorian, Artyom M., and Edward R. Dougherty. 1999. Design and Analysis of Robust Optimal Binary Filters in the Context of a Prior Distribution for the States of Nature. Mathematical Imaging and Vision. 11: 239-254.

    Google Scholar 

  • Heisenberg, Werner. 1952. Philosophic Problems of Nuclear Science. New York: Pantheon.

    Google Scholar 

  • Edwin T. Jaynes. 1968. Prior Probabilities. IEEE Transactions on Systems Science and Cybernetics. 4: 227-241.

    Google Scholar 

  • Kant, Immanuel. 1952 (originally published 1787). Critique of Pure Reason, second edition. Great Books of the Western World, 42, eds. R. M. Hutchins and M. J. Adler. Chicago: Encyclopedia Britannica.

    Google Scholar 

  • Kassam, Saleem, and T. I. Lim. 1977. Robust Wiener Filters. J. Franklin Institute. 304: 171–185.

    Google Scholar 

  • Knight, Jason, Ivan Ivanov, and Edward R. Dougherty. 2014. MCMC Implementation of the Optimal Bayesian Classifier for Non-Gaussian Models: Model-based RNA-Seq Classification. BMC Bioinformatics. 15: 401.

    Google Scholar 

  • Reichenbach, Hans. 1971. The Rise of Scientific Philosophy. Berkeley: University of California Press.

    Google Scholar 

  • Rosenblueth, Arturo, and Norbert Wiener. 1945. The Role of Models in Science. Philosophy of Science. 12: 316-321.

    Google Scholar 

  • Shmulevich, Ilya, and Edward R. Dougherty. 2010. Probabilistic Boolean Networks: The Modeling and Control of Gene Regulatory Networks. New York: SIAM Press.

    Google Scholar 

  • Silver, Edward A. 1963. Markovian Decision Processes with Uncertain Transition Probabilities or Rewards. Technical report. Massachusetts Institute of Technology.

    Google Scholar 

  • Vapnik, Vladimir N., and Alexey Y. Chervonenkis. 1971. On the Uniform Convergence of Relative Frequencies of Events to Their Probabilities. Theory of Probability and its Applications. 16: 264-280.

    Google Scholar 

  • Yoon, Byung-Jun, Xiaoning Qian, and Edward R. Dougherty. 2013. Quantifying the Objective Cost of Uncertainty in Complex Dynamical Systems. IEEE Transactions on Signal Processing. 61: 2256-2266.

    Google Scholar 

  • Zollanvari, Amin, Ulisses M. Braga-Neto, and Edward R. Dougherty. 2011. Analytic Study of Performance of Error Estimators for Linear Discriminant Analysis. IEEE Transactions on Signal Processing. 59: 4238-4255.

    Google Scholar 

  • Zollanvari, Amin, Ulisses M. Braga-Neto, and Edward R. Dougherty. 2012. Exact Representation of the Second-order Moments for Resubstitution and Leave-one-out Error Estimation for Linear Discriminant Analysis in the Univariate Heteroskedastic Gaussian Model. Pattern Recognition. 45: 908-917.

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Texas A&M University College Station, College Station, USA

    Edward R. Dougherty

Authors
  1. Edward R. Dougherty
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Edward R. Dougherty .

Editor information

Editors and Affiliations

  1. TU München, München, Germany

    Wolfgang Pietsch

  2. TU München, München, Germany

    Jörg Wernecke

  3. Anstalt für Kommunale Datenverarbeitung, München, Bayern, Germany

    Maximilian Ott

Rights and permissions

Reprints and permissions

Copyright information

© 2017 Springer Fachmedien Wiesbaden GmbH

About this chapter

Cite this chapter

Dougherty, E.R. (2017). Scientific Epistemology in the Context of Uncertainty. In: Pietsch, W., Wernecke, J., Ott, M. (eds) Berechenbarkeit der Welt?. Springer VS, Wiesbaden. https://doi.org/10.1007/978-3-658-12153-2_6

Download citation

  • DOI: https://doi.org/10.1007/978-3-658-12153-2_6

  • Published:

  • Publisher Name: Springer VS, Wiesbaden

  • Print ISBN: 978-3-658-12152-5

  • Online ISBN: 978-3-658-12153-2

  • eBook Packages: Social Science and Law (German Language)

Publish with us

Policies and ethics

Search

Navigation