%0 Conference Paper %1 VanderPlas2014Frequentism %A VanderPlas, Jake %B 13th PYTHON IN SCIENCE CONF. (SCIPY 2014) %D 2014 %E van der Walt, Stéfan %E Bergstra, James %J Proceedings of the 13th Python in Science Conference %K statistics computing %P 91--99 %T Frequentism and Bayesianism: A Python-driven Primer %U http://conference.scipy.org/proceedings/scipy2014/vanderplas.html %X This paper presents a brief, semi-technical comparison of the essential features of the frequentist and Bayesian approaches to statistical inference, with several illustrative examples implemented in Python. The differences between frequentism and Bayesianism fundamentally stem from differing definitions of probability, a philosophical divide which leads to distinct approaches to the solution of statistical problems as well as contrasting ways of asking and answering questions about unknown parameters. After an example-driven discussion of these differences, we briefly compare several leading Python statistical packages which implement frequentist inference using classical methods and Bayesian inference using Markov Chain Monte Carlo.

@inproceedings{VanderPlas2014Frequentism, abstract = {This paper presents a brief, semi-technical comparison of the essential features of the frequentist and Bayesian approaches to statistical inference, with several illustrative examples implemented in Python. The differences between frequentism and Bayesianism fundamentally stem from differing definitions of probability, a philosophical divide which leads to distinct approaches to the solution of statistical problems as well as contrasting ways of asking and answering questions about unknown parameters. After an example-driven discussion of these differences, we briefly compare several leading Python statistical packages which implement frequentist inference using classical methods and Bayesian inference using Markov Chain Monte Carlo.}, added-at = {2018-06-18T21:23:34.000+0200}, archiveprefix = {arXiv}, author = {VanderPlas, Jake}, biburl = {https://www.bibsonomy.org/bibtex/2982cee1c317f3fe1486f1b6dbf1e0078/pbett}, booktitle = {13th PYTHON IN SCIENCE CONF. (SCIPY 2014)}, citeulike-article-id = {13434549}, citeulike-linkout-0 = {http://conference.scipy.org/proceedings/scipy2014/vanderplas.html}, citeulike-linkout-1 = {http://arxiv.org/abs/1411.5018}, citeulike-linkout-2 = {http://arxiv.org/pdf/1411.5018}, comment = {(private-note)Based on his blog posts at http://jakevdp.github.io/blog/2014/03/11/frequentism-and-bayesianism-a-practical-intro/}, day = 18, editor = {van der Walt, St\'{e}fan and Bergstra, James}, eprint = {1411.5018}, interhash = {7d4b054ec04a8dbfac87c6c7372c62ea}, intrahash = {982cee1c317f3fe1486f1b6dbf1e0078}, journal = {Proceedings of the 13th Python in Science Conference}, keywords = {statistics computing}, month = nov, pages = {91--99}, posted-at = {2016-04-05 14:11:23}, priority = {2}, timestamp = {2018-06-22T18:36:14.000+0200}, title = {Frequentism and Bayesianism: A Python-driven Primer}, url = {http://conference.scipy.org/proceedings/scipy2014/vanderplas.html}, year = 2014 }