What is he best known for?
The fields of study he is best known for:
- 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.
His most cited work include:
- 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)
What are the main themes of his work throughout his whole career to date?
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.
He most often published in these fields:
- Combinatorics (44.96%)
- Discrete mathematics (27.67%)
- Markov chain (25.07%)
What were the highlights of his more recent work (between 2012-2021)?
- Combinatorics (44.96%)
- Discrete mathematics (27.67%)
- Markov chain (25.07%)
In recent papers he was focusing on the following fields of study:
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.
Between 2012 and 2021, his most popular works were:
- Estimating and understanding exponential random graph models (263 citations)
- The sample size required in importance sampling (66 citations)
- Sampling From A Manifold (57 citations)
In his most recent research, the most cited papers focused on:
- 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.
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