Statistical Science

Another Conversation with Persi Diaconis

David Aldous

Full-text: Open access


Persi Diaconis was born in New York on January 31, 1945. Upon receiving a Ph.D. from Harvard in 1974 he was appointed Assistant Professor at Stanford. Following periods as Professor at Harvard (1987–1997) and Cornell (1996–1998), he has been Professor in the Departments of Mathematics and Statistics at Stanford since 1998. He is a member of the National Academy of Sciences, a past President of the IMS and has received honorary doctorates from Chicago and four other universities.

The following conversation took place at his office and at Aldous’s home in early 2012.

Article information

Statist. Sci., Volume 28, Number 2 (2013), 269-281.

First available in Project Euclid: 21 May 2013

Permanent link to this document

Digital Object Identifier

Mathematical Reviews number (MathSciNet)

Zentralblatt MATH identifier

Bayesian statistics card shuffling exchangeability foundations of statistics magic Markov chain Monte Carlo mixing times


Aldous, David. Another Conversation with Persi Diaconis. Statist. Sci. 28 (2013), no. 2, 269--281. doi:10.1214/12-STS404.

Export citation


  • [1] Aldous, D. (1983). Random walks on finite groups and rapidly mixing Markov chains. In Seminar on Probability, XVII. Lecture Notes in Math. 986 243–297. Springer, Berlin.
  • [2] Aldous, D. (2012). On chance and unpredictability: 20 lectures on the links between mathematical probability and the real world. Available at
  • [3] Aldous, D. and Diaconis, P. (1986). Shuffling cards and stopping times. Amer. Math. Monthly 93 333–348.
  • [4] Aldous, D. J. (2010). Exchangeability and continuum limits of discrete random structures. In Proceedings of the International Congress of Mathematicians. Volume I 141–153. Hindustan Book Agency, New Delhi.
  • [5] Bacallado, S. (2011). Bayesian analysis of variable-order, reversible Markov chains. Ann. Statist. 39 838–864.
  • [6] Bayer, D. and Diaconis, P. (1992). Trailing the dovetail shuffle to its lair. Ann. Appl. Probab. 2 294–313.
  • [7] DeGroot, M. H. (1986). A conversation with Persi Diaconis. Statist. Sci. 1 319–334.
  • [8] Diaconis, P. (1977). Finite forms of de Finetti’s theorem on exchangeability. Synthese 36 271–281.
  • [9] Diaconis, P. (1998). A place for philosophy? The rise of modeling in statistical science. Quar. Appl. Math LVI 797–805.
  • [10] Diaconis, P. (2002). G. H. Hardy and probability??? Bull. Lond. Math. Soc. 34 385–402.
  • [11] Diaconis, P. (2009). The Markov chain Monte Carlo revolution. Bull. Amer. Math. Soc. (N.S.) 46 179–205.
  • [12] Diaconis, P. and Freedman, D. (1980). de Finetti’s theorem for Markov chains. Ann. Probab. 8 115–130.
  • [13] Diaconis, P. and Freedman, D. (1980). Finite exchangeable sequences. Ann. Probab. 8 745–764.
  • [14] Diaconis, P. and Freedman, D. (1986). On the consistency of Bayes estimates. Ann. Statist. 14 1–67.
  • [15] Diaconis, P. and Freedman, D. (1987). A dozen de Finetti-style results in search of a theory. Ann. Inst. Henri Poincaré Probab. Stat. 23 397–423.
  • [16] Diaconis, P., Fulman, J. and Holmes, S. (2012). Analysis of casino shelf shuffling machines. Ann. Appl. Probab. To appear.
  • [17] Diaconis, P. and Graham, R. (2011). Magical Mathematics: The Mathematical Ideas that Animate Great Magic Tricks. Princeton Univ. Press, Princeton, NJ.
  • [18] Diaconis, P. and Holmes, S. (2002). A Bayesian peek into Feller volume I. Sankhyā Ser. A 64 820–841.
  • [19] Diaconis, P., Holmes, S. and Montgomery, R. (2007). Dynamical bias in the coin toss. SIAM Rev. 49 211–235.
  • [20] Diaconis, P. and Janson, S. (2008). Graph limits and exchangeable random graphs. Rend. Mat. Appl. (7) 28 33–61.
  • [21] Diaconis, P. and Lebeau, G. (2009). Micro-local analysis for the Metropolis algorithm. Math. Z. 262 411–447.
  • [22] Diaconis, P., Lebeau, G. and Michel, L. (2011). Geometric analysis for the metropolis algorithm on Lipschitz domains. Invent. Math. 185 239–281.
  • [23] Diaconis, P. and Mosteller, F. (1989). Methods for studying coincidences. J. Amer. Statist. Assoc. 84 853–861.
  • [24] Diaconis, P. and Rolles, S. W. W. (2006). Bayesian analysis for reversible Markov chains. Ann. Statist. 34 1270–1292.
  • [25] Diaconis, P. and Saloff-Coste, L. (2012). Convolution powers of complex functions on ${Z}$. Unpublished manuscript.
  • [26] Diaconis, P. and Shahshahani, M. (1981). Generating a random permutation with random transpositions. Z. Wahrsch. Verw. Gebiete 57 159–179.
  • [27] Freedman, D. A. (1962). Mixtures of Markov processes. Ann. Math. Statist. 33 114–118.
  • [28] Freedman, D. A. (2009). Statistical Models: Theory and Practice, revised ed. Cambridge Univ. Press, Cambridge.
  • [29] Gelman, A. and Nolan, D. (2002). You can load a die, but you can’t bias a coin. Amer. Statist. 56 308–311.
  • [30] Geman, S. and Geman, D. (1984). Stochastic relaxation, Gibbs distributions, and the Bayesian restoration of images. IEEE Trans. Pattern Anal. Machine Intelligence 6 721–741.
  • [31] Hammersley, J. M. (1972). A few seedlings of research. In Proceedings of the Sixth Berkeley Symposium on Mathematical Statistics and Probability (Univ. California, Berkeley, Calif., 1970/1971), Vol. I: Theory of Statistics 345–394. Univ. California Press, Berkeley, CA.
  • [32] Jordan, M. I. (2011). What are the open problems in Bayesian statistics? ISBA Bulletin 8 1–4.
  • [33] Ku, P., Larwood, J. and Aldous, D. (2009). 40,000 coin tosses yield ambiguous evidence for dynamical bias. Available at
  • [34] Levin, D. A., Peres, Y. and Wilmer, E. L. (2009). Markov Chains and Mixing Times. Amer. Math. Soc., Providence, RI.
  • [35] Pratt, J. and Schlaifer, R. (1985). Repetitive assessment of judgmental probability distributions: A case study. In Proc. Second Valencia International Meeting on Bayesian Statistics 393–424. North-Holland, Amsterdam.
  • [36] Strzalko, J., Grabski, J., Stefanski, A., Perlikowski, P. and Kapitaniak, T. (2009). Dynamics of Gambling: Origins of Randomness in Dynamical Systems. Springer, New York.
  • [37] von Neumann, J. (1947). The mathematician. In The Works of the Mind 180–196. Univ. Chicago Press, Chicago, IL.
  • [38] Yong, E. and Mahadevan, L. (2011). Probability and dynamics in the toss of a thick coin. Available at arXiv:1008.4559.