What is he best known for?
The fields of study he is best known for:
- 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.
His most cited work include:
- Lectures on Algebraic Statistics (257 citations)
- Model selection for Gaussian concentration graphs (167 citations)
- Sequential Monte Carlo Methods (141 citations)
What are the main themes of his work throughout his whole career to date?
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.
He most often published in these fields:
- Graphical model (31.98%)
- Applied mathematics (31.47%)
- Algorithm (17.77%)
What were the highlights of his more recent work (between 2016-2021)?
- Applied mathematics (31.47%)
- Graphical model (31.98%)
- Graph (12.69%)
In recent papers he was focusing on the following fields of study:
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.
Between 2016 and 2021, his most popular works were:
- Structure Learning in Graphical Modeling (105 citations)
- A Bayesian information criterion for singular models (56 citations)
- Handbook of Graphical Models (37 citations)
In his most recent research, the most cited papers focused on:
- 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.
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