What is he best known for?
The fields of study he is best known for:
Alexei J. Drummond mostly deals with Bayes' theorem, Bayesian probability, Coalescent theory, Markov chain Monte Carlo and Phylogenetic tree.
His studies deal with areas such as Algorithm, Viral phylodynamics, Prior probability and Markov chain as well as Bayes' theorem.
His Bayesian probability research incorporates elements of Data science, Inference and Bioinformatics.
His Coalescent theory study combines topics in areas such as Evolutionary biology and Statistics.
Alexei J. Drummond interconnects Statistical inference and Bayesian inference in the investigation of issues within Markov chain Monte Carlo.
Ecology is closely connected to Phylogenetics in his research, which is encompassed under the umbrella topic of Phylogenetic tree.
His most cited work include:
- BEAST: Bayesian evolutionary analysis by sampling trees (10037 citations)
- Bayesian Phylogenetics with BEAUti and the BEAST 1.7 (7195 citations)
- Relaxed Phylogenetics and Dating with Confidence (4729 citations)
What are the main themes of his work throughout his whole career to date?
His scientific interests lie mostly in Bayesian probability, Phylogenetic tree, Coalescent theory, Evolutionary biology and Inference.
His work carried out in the field of Bayesian probability brings together such families of science as Tree and Algorithm.
His Phylogenetic tree research incorporates themes from Paleontology, Phylogenetics and Computational biology.
While the research belongs to areas of Coalescent theory, Alexei J. Drummond spends his time largely on the problem of Population size, intersecting his research to questions surrounding Theoretical computer science and Selection.
His work deals with themes such as Population genetics, Gene flow, Genetic diversity, Viral phylodynamics and Phylogeography, which intersect with Evolutionary biology.
His Bayes' theorem research is multidisciplinary, relying on both Markov chain and Bioinformatics.
He most often published in these fields:
- Bayesian probability (29.73%)
- Phylogenetic tree (27.57%)
- Coalescent theory (25.41%)
What were the highlights of his more recent work (between 2016-2021)?
- Coalescent theory (25.41%)
- Bayesian probability (29.73%)
- Inference (21.62%)
In recent papers he was focusing on the following fields of study:
Coalescent theory, Bayesian probability, Inference, Phylogenetic tree and Evolutionary biology are his primary areas of study.
His Coalescent theory research focuses on Concatenation and how it connects with Estimator, Heuristic and Molecular sequence.
His work on Posterior probability, Bayesian inference and Markov chain Monte Carlo as part of general Bayesian probability study is frequently connected to Hierarchy, therefore bridging the gap between diverse disciplines of science and establishing a new relationship between them.
His studies in Inference integrate themes in fields like Mutation, Tree, Phylogenetics, Multilocus sequence typing and Software.
His biological study spans a wide range of topics, including Statistics, Computational biology and Single species.
He has included themes like Phylogeography, Gene flow and Viral phylodynamics in his Evolutionary biology study.
Between 2016 and 2021, his most popular works were:
- Posterior Summarization in Bayesian Phylogenetics Using Tracer 1.7. (2174 citations)
- Bayesian phylogenetic and phylodynamic data integration using BEAST 1.10 (856 citations)
- BEAST 2.5: An advanced software platform for Bayesian evolutionary analysis. (591 citations)
In his most recent research, the most cited papers focused on:
Alexei J. Drummond spends much of his time researching Bayesian probability, Coalescent theory, Phylogenetic tree, Inference and Bayesian inference.
His work on Posterior probability, Markov chain Monte Carlo and Bayes' theorem as part of general Bayesian probability study is frequently linked to Extant taxon, therefore connecting diverse disciplines of science.
His Coalescent theory research integrates issues from Algorithm, Genetic Change, Computational biology and H1N1 influenza.
His Phylogenetic tree study is concerned with the field of Genetics as a whole.
His research investigates the link between Inference and topics such as Phylogenetics that cross with problems in Ecology and Big data.
His research on Bayesian inference frequently links to adjacent areas such as Evolutionary biology.
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