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- Persi Diaconis

Mathematics

USA

2023

Discipline name
D-index
D-index (Discipline H-index) only includes papers and citation values for an examined
discipline in contrast to General H-index which accounts for publications across all
disciplines.
Citations
Publications
World Ranking
National Ranking

Mathematics
D-index
84
Citations
33,948
338
World Ranking
69
National Ranking
41

2023 - Research.com Mathematics in United States Leader Award

2013 - Fellow of the American Mathematical Society

2000 - John von Neumann Lecturer

1995 - Member of the National Academy of Sciences

1994 - Fellow of the American Statistical Association (ASA)

1989 - Fellow of the American Academy of Arts and Sciences

1987 - Wald Memorial Lecturer

1982 - Fellow of the MacArthur Foundation

- Statistics
- Combinatorics
- Algebra

His primary areas of study are Combinatorics, Discrete mathematics, Markov chain, Markov chain mixing time and Random walk. Specifically, his work in Combinatorics is concerned with the study of Random permutation. His Discrete mathematics research incorporates themes from Sufficient statistic, Simple and Markov process.

The concepts of his Markov chain study are interwoven with issues in Statistical physics, Mathematical analysis, Pure mathematics and Markov chain Monte Carlo. His work carried out in the field of Mathematical analysis brings together such families of science as Random matrix and Haar measure. His Random walk study integrates concerns from other disciplines, such as Eigenvalues and eigenvectors and Stationary distribution.

- Group representations in probability and statistics (1138 citations)
- Computer-Intensive Methods in Statistics (933 citations)
- On the histogram as a density estimator: L 2 theory (856 citations)

Persi Diaconis focuses on Combinatorics, Discrete mathematics, Markov chain, Eigenvalues and eigenvectors and Random walk. Persi Diaconis is involved in the study of Combinatorics that focuses on Symmetric group in particular. As part of his studies on Discrete mathematics, Persi Diaconis often connects relevant areas like Limit.

The various areas that Persi Diaconis examines in his Markov chain study include Statistical physics and Applied mathematics. His Random walk research includes themes of Pure mathematics and Stationary distribution. His research integrates issues of Examples of Markov chains and Additive Markov chain in his study of Markov chain mixing time.

- Combinatorics (44.96%)
- Discrete mathematics (27.67%)
- Markov chain (25.07%)

- Combinatorics (44.96%)
- Discrete mathematics (27.67%)
- Markov chain (25.07%)

His primary areas of investigation include Combinatorics, Discrete mathematics, Markov chain, Eigenvalues and eigenvectors and Pure mathematics. His biological study spans a wide range of topics, including Central limit theorem and Group. Persi Diaconis has researched Discrete mathematics in several fields, including Exponential family, Expected value, Sample size determination and Importance sampling.

The study incorporates disciplines such as Chain, Quantum group, Boundary and Applied mathematics in addition to Markov chain. His work deals with themes such as Ergodic theory and Markov chain Monte Carlo, which intersect with Applied mathematics. His research in Eigenvalues and eigenvectors intersects with topics in Matrix and Absorbing Markov chain.

- Estimating and understanding exponential random graph models (263 citations)
- The sample size required in importance sampling (66 citations)
- Sampling From A Manifold (57 citations)

- Statistics
- Algebra
- Combinatorics

His scientific interests lie mostly in Combinatorics, Discrete mathematics, Markov chain, Sample size determination and Importance sampling. His studies deal with areas such as Bivariate analysis and Asymptotic distribution as well as Combinatorics. The Discrete mathematics study combines topics in areas such as Exponential family, Central limit theorem and Mathematical proof.

His study in Exponential family is interdisciplinary in nature, drawing from both Geometric measure theory, Sampling, Manifold, Goodness of fit and Conditional probability distribution. His Markov chain research is multidisciplinary, incorporating perspectives in Markov chain Monte Carlo, Kravchuk polynomials, Multinomial distribution, Multivariate statistics and Orthogonal polynomials. His study on Sample size determination also encompasses disciplines like

- Bipartite graph which intersects with area such as Theoretical computer science,
- Logarithmic scale and related Gibbs measure, Sample, Function and Probability and statistics.

This overview was generated by a machine learning system which analysed the scientist’s body of work. If you have any feedback, you can contact us here.

Group representations in probability and statistics

Persi Diaconis.

**(1988)**

2119 Citations

Computer-Intensive Methods in Statistics

Persi Diaconis;Bradley Efron.

Scientific American **(1983)**

1626 Citations

On the histogram as a density estimator:L 2 theory

David Freedman;Persi Diaconis.

Probability Theory and Related Fields **(1981)**

1617 Citations

Geometric Bounds for Eigenvalues of Markov Chains

Persi Diaconis;Daniel Stroock.

Annals of Applied Probability **(1991)**

1107 Citations

On the Consistency of Bayes Estimates

Persi Diaconis;David Freedman.

Annals of Statistics **(1986)**

914 Citations

Conjugate Priors for Exponential Families

Persi Diaconis;Donald Ylvisaker.

Annals of Statistics **(1979)**

858 Citations

Algebraic algorithms for sampling from conditional distributions

Persi Diaconis;Bernd Sturmfels.

Annals of Statistics **(1998)**

858 Citations

Asymptotics of Graphical Projection Pursuit

Persi Diaconis;David Freedman.

Annals of Statistics **(1984)**

791 Citations

Spearman's Footrule as a Measure of Disarray

Persi Diaconis;R. L. Graham.

Journal of the royal statistical society series b-methodological **(1977)**

770 Citations

Fastest mixing Markov chain on a graph

Stephen Boyd;Persi Diaconis;Lin Xiao.

**(2003)**

758 Citations

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