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.