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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.
H. Jeffreys An Invariant Form for the Prior Probability in Estimation Problems . Proc. of the Royal Society of London. Series A 186(1007): 453, 1946
1946
Earlier work this paper cites.
E.T. Jaynes. Confidence Intervals vs Bayesian Intervals (1976) Papers on Probability, Statistics and Statistical Physics Synthese Library 158:149, 1989
1989
Earlier work this paper cites.
S.N. Evans & P.B. Stark. Inverse Problems as Statistics . Mathematics Statistics Library, 609, 2002
2002
Earlier work this paper cites.
M. Hardy. An illuminating counterexample . Am. Math. Monthly 110:234–238, 2003
2003
Earlier work this paper cites.
S.R. Eddy. What is Bayesian statistics? . Nature Biotechnology 22:1177-1178, 2004
2004
Earlier work this paper cites.
A. Gelman, J.B. Carlin, H.S. Stern, and D.B. Rubin. Bayesian Data Analysis, Second Edition. Chapman and Hall/CRC, Boca Raton, FL, 2004
2004
Cited alongside, same era.
L. Wasserman. All of statistics: a concise course in statistical inference . Springer, 2004
2004
Cited alongside, same era.
J. Goodman & J. Weare. Ensemble Samplers with Affine Invariance . Comm. in Applied Mathematics and Computational Science 5(1):65-80, 2010
2010
Cited alongside, same era.
A. Patil, D. Huard, C.J. Fonnesbeck. PyMC: Bayesian Stochastic Modelling in Python Journal of Statistical Software, 35(4):1-81, 2010
2010
Cited alongside, same era.
T. Bayes. An essay towards solving a problem in the doctrine of chances . Philosophical Transactions of the Royal Society of London 53(0):370-418, 1763
Cited in the paper.
J.S. Seabold and J. Perktold. Statsmodels: Econometric and Statistical Modeling with Python Proceedings of the 9th Python in Science Conference, 2010
2010
Later among the works it cites.
D. Foreman-Mackey, D.W. Hogg, D. Lang, J.Goodman. emcee: the MCMC Hammer . PASP 125(925):306-312, 2014
2014
Closest in time.
M.C. Hoffman & A. Gelman. The No-U-Turn Sampler: Adaptively Setting Path Lengths in Hamiltonian Monte Carlo . JMLR, submitted, 2014
2014
Closest in time.
J. VanderPlas. Frequentism and Bayesianism . Four-part series ( I , II , III , IV ) on Pythonic Perambulations http://jakevdp.github.io/ , 2014
2014
Closest in time.
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