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- Mathias Drton

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
31
Citations
4,152
122
World Ranking
2594
National Ranking
155

2009 - Fellow of Alfred P. Sloan Foundation

- Statistics
- Algebra
- Normal distribution

Mathias Drton mainly investigates Graphical model, Model selection, Algorithm, Combinatorics and Statistical model. Mathias Drton interconnects Theoretical computer science, Markov chain, Bidirected graph, Conditional probability distribution and Likelihood function in the investigation of issues within Graphical model. While the research belongs to areas of Likelihood function, he spends his time largely on the problem of Markov property, intersecting his research to questions surrounding Applied mathematics and Multivariate normal distribution.

His Model selection research incorporates elements of Markov chain Monte Carlo, Partial correlation, Rank, Propagation of uncertainty and Gibbs sampling. His research investigates the link between Algorithm and topics such as Consistency that cross with problems in Sample size determination, Information Criteria, Bayesian information criterion and Lasso. His research integrates issues of Parameter space, Graph and Bayesian network in his study of Statistical model.

- Lectures on Algebraic Statistics (257 citations)
- Model selection for Gaussian concentration graphs (167 citations)
- Sequential Monte Carlo Methods (141 citations)

The scientist’s investigation covers issues in Graphical model, Applied mathematics, Algorithm, Mixed graph and Combinatorics. His biological study spans a wide range of topics, including Exponential family, Graph, Theoretical computer science, Conditional independence and Markov chain. The Applied mathematics study combines topics in areas such as Covariance, Covariance matrix, Identifiability, Multivariate normal distribution and Likelihood function.

His Algorithm study incorporates themes from Model selection, Artificial intelligence, Expectation–maximization algorithm, Consistency and Pattern recognition. Mathias Drton usually deals with Model selection and limits it to topics linked to Bayesian information criterion and Information Criteria. His study in Mixed graph is interdisciplinary in nature, drawing from both Structural equation modeling and Null graph.

- Graphical model (31.98%)
- Applied mathematics (31.47%)
- Algorithm (17.77%)

- Applied mathematics (31.47%)
- Graphical model (31.98%)
- Graph (12.69%)

His primary areas of study are Applied mathematics, Graphical model, Graph, Mixed graph and Covariance. His Applied mathematics research integrates issues from Matching, Inference, Variable, Structural equation modeling and Likelihood function. His studies in Graphical model integrate themes in fields like Exponential family, Probability density function, Orthant, Numerical integration and Normalizing constant.

His Graph study combines topics from a wide range of disciplines, such as Identifiability, Directed acyclic graph, Feature and Directed graph. His studies deal with areas such as Test statistic, Null distribution, Covariance matrix, Multivariate statistics and Statistic as well as Covariance. He has researched Covariance matrix in several fields, including Polynomial, Algebra and Combinatorics.

- Structure Learning in Graphical Modeling (105 citations)
- A Bayesian information criterion for singular models (56 citations)
- Handbook of Graphical Models (37 citations)

- Statistics
- Algebra
- Normal distribution

Mathias Drton mainly focuses on Applied mathematics, Graphical model, Graph, Covariance and Statistics. His Applied mathematics research is multidisciplinary, incorporating perspectives in Matching, Numerical integration, Probability density function and Normalizing constant. His study looks at the relationship between Graphical model and fields such as Statistical model, as well as how they intersect with chemical problems.

He works mostly in the field of Graph, limiting it down to topics relating to Latent variable and, in certain cases, Contrast, Algorithm, Equivalence class and Bayesian network, as a part of the same area of interest. The various areas that Mathias Drton examines in his Covariance study include Mixed graph, Algebra, Covariance matrix, Multivariate statistics and Statistic. His work on Distribution free, Independence test and Test as part of general Statistics study is frequently connected to Independence, therefore bridging the gap between diverse disciplines of science and establishing a new relationship between them.

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.

Lectures on Algebraic Statistics

Mathias Drton;Bernd Sturmfels;Seth Sullivant.

**(2008)**

536 Citations

Extended Bayesian Information Criteria for Gaussian Graphical Models

Rina Foygel;Mathias Drton.

neural information processing systems **(2010)**

259 Citations

Model selection for Gaussian concentration graphs

Mathias Drton;Michael D. Perlman.

Biometrika **(2004)**

244 Citations

Structure Learning in Graphical Modeling

Mathias Drton;Marloes H. Maathuis.

Social Science Research Network **(2017)**

168 Citations

Estimation of a covariance matrix with zeros

Sanjay Chaudhuri;Mathias Drton;Thomas S. Richardson.

Biometrika **(2007)**

159 Citations

Multiple Testing and Error Control in Gaussian Graphical Model Selection

Mathias Drton;Michael D. Perlman.

Statistical Science **(2007)**

152 Citations

Discrete chain graph models

Mathias Drton.

Bernoulli **(2009)**

132 Citations

A SINful approach to Gaussian graphical model selection

Mathias Drton;Michael D. Perlman.

Journal of Statistical Planning and Inference **(2008)**

132 Citations

PC algorithm for nonparanormal graphical models

Naftali Harris;Mathias Drton.

Journal of Machine Learning Research **(2013)**

125 Citations

Likelihood ratio tests and singularities

Mathias Drton.

Annals of Statistics **(2009)**

120 Citations

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