What is he best known for?
The fields of study he is best known for:
- Statistics
- Artificial intelligence
- Normal distribution
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 most cited work include:
- Bayesian Data Analysis (13605 citations)
- High cumulative incidence of uterine leiomyoma in black and white women: Ultrasound evidence☆☆☆★ (1344 citations)
- Changes with age in the level and duration of fertility in the menstrual cycle (411 citations)
What are the main themes of his work throughout his whole career to date?
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.
He most often published in these fields:
- Bayesian probability (30.64%)
- Algorithm (20.84%)
- Artificial intelligence (20.53%)
What were the highlights of his more recent work (between 2017-2021)?
- Bayesian probability (30.64%)
- Algorithm (20.84%)
- Markov chain Monte Carlo (14.62%)
In recent papers he was focusing on the following fields of study:
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.
Between 2017 and 2021, his most popular works were:
- A comprehensive evaluation of predictive performance of 33 species distribution models at species and community levels (85 citations)
- Robust Bayesian Inference via Coarsening (73 citations)
- Scalable Bayes via Barycenter in Wasserstein Space (60 citations)
In his most recent research, the most cited papers focused on:
- Statistics
- Artificial intelligence
- Machine learning
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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