Abstract
In this chapter we explore an alternative interpretation of statistics – Bayesian statistics – and the methods associated with this interpretation. Bayesian statistics, in contrast to the frequentist’s statistics that we used in Chapter 13 and Chapter 14, treat probability as a degree of belief rather than as a measure of proportions of observed outcomes. This different point of view gives rise to distinct statistical methods that can be used in problem solving. While it is generally true that statistical problems can in principle be solved using either frequentist or Bayesian statistics, there are practical differences that make these two approaches to statistics suitable for different types of problems
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Notes
- 1.
See also the Slice, HamiltonianMC, and NUTS samplers, which can be used more or less interchangeably.
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© 2015 Robert Johansson
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Johansson, R. (2015). Bayesian Statistics. In: Numerical Python. Apress, Berkeley, CA. https://doi.org/10.1007/978-1-4842-0553-2_16
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DOI: https://doi.org/10.1007/978-1-4842-0553-2_16
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