Bibliography
Agresti, A., and M. Kateri. 2022. Foundations of Statistics for Data
Scientists. Chapman; Hall/CRC.
Diaconis, P., and B. Skyrms. 2018. Ten Great Ideas about
Chance. Princeton University Press.
Domingos, P. 2015. The Master Algorithm: How the Quest for the
Ultimate Learning Machine Will Remake Our World. Basic Books.
Gelman, A., J. B. Carlin, H. A. Stern, D. B. Dunson, A. Vehtari, and D.
B. Rubin. 2014. Bayesian Data Analysis. 3rd ed. CRC Press.
Heard, N. 2021. An Introduction to Bayesian Inference, Methods and
Computation. Springer.
Held, L., and D. S. Bové. 2020. Applied Statistical Inference:
Likelihood and Bayes. Second. Springer.
McGrayne, S. B. 2011. The Theory That Would Not Die. Yale
University Press.