Bayesian Statistics, a technique that has become very popular for many types of machine learning, starts out with a new view at statistical data: it takes the observed data as fixed, and looks at the likelihood to find certain model parameters. This chapter introduces Bayesian Statistics, and provides a worked example using the Python package “PyMC,” showing how Bayesian Statistics can provide more information than classical statistical modeling.
KeywordsBayesian Statistics Modeling Gaussian Process Prior Odds Python Package Frequentist Interpretation
- Bishop, C. M. (2007). Pattern recognition and machine learning. New York: Springer.Google Scholar
- Duda, R. O., Hart, P. E., & Stork, D. G. (2004). Pattern classification (2nd ed.). Hoboken: Wiley-Interscience.Google Scholar
- Pilon, C. D. (2015). Probabilistic programming and Bayesian methods for hackers. http://camdavidsonpilon.github.io/Probabilistic-Programming-and-Bayesian-Methods-for-Hackers/ Google Scholar