Numerical Python: Scientific Computing and Data Science Applications with Numpy, SciPy and Matplotlib

© Robert Johansson 2019

Robert JohanssonNumerical Python https://doi.org/10.1007/978-1-4842-4246-9_16

16. Bayesian Statistics

Robert Johansson¹ 

(1)

Urayasu-shi, Chiba, Japan

 

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, treats 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 we can use in problem-solving. While it is generally true that statistical problems can in principle be solved using either frequentist or Bayesian ...