Skip to main content

Bayesian Statistics (with Particular Focus on the Motor System)

  • Reference work entry
  • First Online:
Encyclopedia of Neuroscience
  • 417 Accesses

Synonyms

Probabilistic inference; Statistical inference; Bayesian inference; Antonym: Frequentist statistics

Definition

As our sensors are not perfect and do not provide information about all the properties of the world, we are faced with sensory uncertainty. Moreover, our muscles also produce noisy outputs and so we are also faced with motor uncertainty. Bayesian statistics is the systematic way of dealing with uncertainty by expressing its many forms in terms of probability.

Description of the Theory

Bayesian statistics can be used to infer the states of variables that are not directly measured, a process called Bayesian inference. “For example, when we see a tennis ball (observed variable) and we know from experience how they usually fly (prior knowledge) then Bayesian statistics allows us to calculate how likely the ball will land at any given position on the field (unobserved variable).” The way inference is done is the following:

(i) The prior knowledgeabout the system is...

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

Access this chapter

Institutional subscriptions

References

  1. Cox RT (1946) Probability, frequency and reasonable expectation. Am J Phys 17:1–13

    Article  Google Scholar 

  2. Freedman DA (1995) Some issues in the foundation of statistics. Found Sci 1:19–83

    Article  Google Scholar 

  3. Körding KP, Wolpert DM (2004) Bayesian integration in sensorimotor learning. Nature 427:244–247

    Article  PubMed  Google Scholar 

  4. Ernst MO, Bulthoff HH (2004) Merging the senses into a robust percept. Trends Cogn Sci 8:162–169

    Article  PubMed  Google Scholar 

  5. Kording K (2007) Decision theory: what “should” the nervous system do? Science 318:606–610

    Article  PubMed  Google Scholar 

  6. Wolpert DM, Ghahramani Z, Jordan MI (1995) An internal model for sensorimotor integration. Science 269:1880–1882

    Article  CAS  PubMed  Google Scholar 

  7. Kalman RE (1960) A new approach to linear filtering and prediction problems. J Basic Eng (ASME) 82D:35–45

    Article  Google Scholar 

  8. Trommershauser J, Maloney LT, Landy MS (2003) Statistical decision theory and the selection of rapid, goal-directed movements. J Opt Soc Am A Opt Image Sci Vis 20:1419–1433

    Article  PubMed  Google Scholar 

  9. MacKay DJC (2003) Information Theory, Inference, and Learning Algorithms. Cambridge University press, Cambridge, UK [also available online at http://www.inference.phy.cam.ac.uk/mackay/itila/book.html]

  10. Kording KP, Beierholm U, Ma WJ, Quartz S, Tenenbaum JB, Shams L (2007) Causal inference in multisensory percption. PLoS ONE 2:e943

    Article  PubMed Central  PubMed  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2008 Springer-Verlag GmbH Berlin Heidelberg

About this entry

Check for updates. Verify currency and authenticity via CrossMark

Cite this entry

Körding, K. (2008). Bayesian Statistics (with Particular Focus on the Motor System). In: Binder, M.D., Hirokawa, N., Windhorst, U. (eds) Encyclopedia of Neuroscience. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-29678-2_578

Download citation

Publish with us

Policies and ethics