Abstract
This short chapter describes the relationship between computer science and creditions, and shows that any subfield of computer science that deals with data needs to make assumptions, and that these assumptions (we call them priors) are nothing else than beliefs or creditions. Two simple examples from computer vision and machine learning demonstrate the effect of priors. Finally, Bayesian inference is introduced as a principle method to handle priors in a rigorous manner.
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Notes
- 1.
To simplify the notation we skip the two-dimensional indices when there is no danger of confusion.
- 2.
This was the assumption to obtain the simple solution in Fig. 3.
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Bischof, H. (2017). Creditions and Modern Computer Science. In: Angel, HF., Oviedo, L., Paloutzian, R., Runehov, A., Seitz, R. (eds) Processes of Believing: The Acquisition, Maintenance, and Change in Creditions. New Approaches to the Scientific Study of Religion , vol 1. Springer, Cham. https://doi.org/10.1007/978-3-319-50924-2_28
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DOI: https://doi.org/10.1007/978-3-319-50924-2_28
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