2023 - Research.com Mathematics in United States Leader Award
2010 - COPSS Presidents' Award For his wide-ranging and fundamental contributions to the development of parametric and nonparametric modeling within complex Bayesian frameworks; for making significant concurrent scientific progress in machine learning through this development; for use of this methodology in substantive applications, notably in reproductive epidemiology; and for outstanding service to the profession as well as mentoring of students and post-doctoral researchers.
2007 - Fellow of the American Statistical Association (ASA)
His scientific interests lie mostly in Statistics, Bayesian probability, Applied mathematics, Prior probability and Algorithm. Bayesian probability is closely attributed to Data mining in his study. His Applied mathematics research includes elements of Lasso, Convergence of random variables, Multivariate random variable and Random graph.
His study in Algorithm is interdisciplinary in nature, drawing from both Nonparametric statistics, Dirichlet process and Markov chain Monte Carlo. David B. Dunson has researched Dirichlet process in several fields, including Marginal likelihood, Hierarchical Dirichlet process and Markov chain. Dynamic Bayesian network and Probabilistic programming language is closely connected to Bayesian statistics in his research, which is encompassed under the umbrella topic of Variable-order Bayesian network.
His primary scientific interests are in Bayesian probability, Algorithm, Artificial intelligence, Statistics and Applied mathematics. The concepts of his Bayesian probability study are interwoven with issues in Inference and Data mining. His work in Algorithm tackles topics such as Markov chain Monte Carlo which are related to areas like Markov chain.
The study incorporates disciplines such as Machine learning and Pattern recognition in addition to Artificial intelligence. David B. Dunson performs multidisciplinary study in Applied mathematics and Gaussian process in his work. As a member of one scientific family, David B. Dunson mostly works in the field of Prior probability, focusing on Dirichlet process and, on occasion, Mixture model.
David B. Dunson mainly focuses on Bayesian probability, Algorithm, Markov chain Monte Carlo, Artificial intelligence and Inference. His Bayesian probability research incorporates themes from Mixture model, Multivariate statistics, Data mining and Cluster analysis. His Markov chain Monte Carlo course of study focuses on Bayesian inference and Scalability.
His biological study spans a wide range of topics, including Machine learning and Pattern recognition. His research in Inference intersects with topics in Statistics and Latent variable. His study looks at the intersection of Prior probability and topics like Applied mathematics with Eigenvalues and eigenvectors and Rate of convergence.
His primary areas of study are Bayesian probability, Bayesian inference, Markov chain Monte Carlo, Artificial intelligence and Algorithm. His studies in Bayesian probability integrate themes in fields like Mixture model, Conditional probability distribution and Inference. His Bayesian inference study combines topics in areas such as Sampling and Scalability.
His studies deal with areas such as Embedding, Orthogonal matrix, Applied mathematics and Stiefel manifold as well as Markov chain Monte Carlo. David B. Dunson has included themes like Rate of convergence, Eigenvalues and eigenvectors and Hybrid Monte Carlo in his Applied mathematics study. His study looks at the relationship between Cluster analysis and topics such as MNIST database, which overlap with Data mining.
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Bayesian Data Analysis
Andrew Gelman;John B. Carlin;Hal S. Stern;David B. Dunson.
High cumulative incidence of uterine leiomyoma in black and white women: Ultrasound evidence☆☆☆★
Donna Day Baird;David B. Dunson;Michael C. Hill;Deborah Cousins.
American Journal of Obstetrics and Gynecology (2003)
Genetic and Functional Drivers of Diffuse Large B Cell Lymphoma.
Anupama Reddy;Jenny Zhang;Nicholas S. Davis;Andrea B. Moffitt.
Changes with age in the level and duration of fertility in the menstrual cycle
David B. Dunson;Bernardo Colombo;Donna D. Baird.
Human Reproduction (2002)
Bayesian data analysis, third edition
A Gelman;JB Carlin;HS Stern;DB Dunson.
Increased infertility with age in men and women.
David B. Dunson;Donna D. Baird;Bernardo Colombo.
Obstetrics & Gynecology (2004)
Multitask Compressive Sensing
S. Ji;D. Dunson;L. Carin.
IEEE Transactions on Signal Processing (2009)
The genetic landscape of mutations in Burkitt lymphoma
Cassandra Love;Zhen Sun;Dereje Jima;Guojie Li.
Nature Genetics (2012)
Genetic heterogeneity of diffuse large B-cell lymphoma
Jenny Zhang;Vladimir Grubor;Cassandra L. Love;Anjishnu Banerjee.
Proceedings of the National Academy of Sciences of the United States of America (2013)
The timing of the “fertile window” in the menstrual cycle: day specific estimates from a prospective study
Allen J Wilcox;David Dunson;Donna Day Baird.
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