The Experts below are selected from a list of 200811 Experts worldwide ranked by ideXlab platform
Remco R Bouckaert - One of the best experts on this subject based on the ideXlab platform.
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beast 2 5 an advanced Software Platform for bayesian evolutionary analysis
PLOS Computational Biology, 2019Co-Authors: Remco R Bouckaert, Timothy G Vaughan, Joelle Baridosottani, Sebastian Duchene, Mathieu Fourment, Alexandra Gavryushkina, Joseph HeledAbstract:Elaboration of Bayesian phylogenetic inference methods has continued at pace in recent years with major new advances in nearly all aspects of the joint modelling of evolutionary data. It is increasingly appreciated that some evolutionary questions can only be adequately answered by combining evidence from multiple independent sources of data, including genome sequences, sampling dates, phenotypic data, radiocarbon dates, fossil occurrences, and biogeographic range information among others. Including all relevant data into a single joint model is very challenging both conceptually and computationally. Advanced computational Software packages that allow robust development of compatible (sub-)models which can be composed into a full model hierarchy have played a key role in these developments. Developing such Software frameworks is increasingly a major scientific activity in its own right, and comes with specific challenges, from practical Software design, development and engineering challenges to statistical and conceptual modelling challenges. BEAST 2 is one such computational Software Platform, and was first announced over 4 years ago. Here we describe a series of major new developments in the BEAST 2 core Platform and model hierarchy that have occurred since the first release of the Software, culminating in the recent 2.5 release.
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beast 2 a Software Platform for bayesian evolutionary analysis
PLOS Computational Biology, 2014Co-Authors: Remco R Bouckaert, Joseph Heled, Denise Kuhnert, Timothy G Vaughan, Chiehhsi Wu, Marc A Suchard, Andrew Rambaut, Alexei J DrummondAbstract:We present a new open source, extensible and flexible Software Platform for Bayesian evolutionary analysis called BEAST 2. This Software Platform is a re-design of the popular BEAST 1 Platform to correct structural deficiencies that became evident as the BEAST 1 Software evolved. Key among those deficiencies was the lack of post-deployment extensibility. BEAST 2 now has a fully developed package management system that allows third party developers to write additional functionality that can be directly installed to the BEAST 2 analysis Platform via a package manager without requiring a new Software release of the Platform. This package architecture is showcased with a number of recently published new models encompassing birth-death-sampling tree priors, phylodynamics and model averaging for substitution models and site partitioning. A second major improvement is the ability to read/write the entire state of the MCMC chain to/from disk allowing it to be easily shared between multiple instances of the BEAST Software. This facilitates checkpointing and better support for multi-processor and high-end computing extensions. Finally, the functionality in new packages can be easily added to the user interface (BEAUti 2) by a simple XML template-based mechanism because BEAST 2 has been re-designed to provide greater integration between the analysis engine and the user interface so that, for example BEAST and BEAUti use exactly the same XML file format.
Joseph Heled - One of the best experts on this subject based on the ideXlab platform.
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beast 2 5 an advanced Software Platform for bayesian evolutionary analysis
PLOS Computational Biology, 2019Co-Authors: Remco R Bouckaert, Timothy G Vaughan, Joelle Baridosottani, Sebastian Duchene, Mathieu Fourment, Alexandra Gavryushkina, Joseph HeledAbstract:Elaboration of Bayesian phylogenetic inference methods has continued at pace in recent years with major new advances in nearly all aspects of the joint modelling of evolutionary data. It is increasingly appreciated that some evolutionary questions can only be adequately answered by combining evidence from multiple independent sources of data, including genome sequences, sampling dates, phenotypic data, radiocarbon dates, fossil occurrences, and biogeographic range information among others. Including all relevant data into a single joint model is very challenging both conceptually and computationally. Advanced computational Software packages that allow robust development of compatible (sub-)models which can be composed into a full model hierarchy have played a key role in these developments. Developing such Software frameworks is increasingly a major scientific activity in its own right, and comes with specific challenges, from practical Software design, development and engineering challenges to statistical and conceptual modelling challenges. BEAST 2 is one such computational Software Platform, and was first announced over 4 years ago. Here we describe a series of major new developments in the BEAST 2 core Platform and model hierarchy that have occurred since the first release of the Software, culminating in the recent 2.5 release.
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beast 2 a Software Platform for bayesian evolutionary analysis
PLOS Computational Biology, 2014Co-Authors: Remco R Bouckaert, Joseph Heled, Denise Kuhnert, Timothy G Vaughan, Chiehhsi Wu, Marc A Suchard, Andrew Rambaut, Alexei J DrummondAbstract:We present a new open source, extensible and flexible Software Platform for Bayesian evolutionary analysis called BEAST 2. This Software Platform is a re-design of the popular BEAST 1 Platform to correct structural deficiencies that became evident as the BEAST 1 Software evolved. Key among those deficiencies was the lack of post-deployment extensibility. BEAST 2 now has a fully developed package management system that allows third party developers to write additional functionality that can be directly installed to the BEAST 2 analysis Platform via a package manager without requiring a new Software release of the Platform. This package architecture is showcased with a number of recently published new models encompassing birth-death-sampling tree priors, phylodynamics and model averaging for substitution models and site partitioning. A second major improvement is the ability to read/write the entire state of the MCMC chain to/from disk allowing it to be easily shared between multiple instances of the BEAST Software. This facilitates checkpointing and better support for multi-processor and high-end computing extensions. Finally, the functionality in new packages can be easily added to the user interface (BEAUti 2) by a simple XML template-based mechanism because BEAST 2 has been re-designed to provide greater integration between the analysis engine and the user interface so that, for example BEAST and BEAUti use exactly the same XML file format.
Timothy G Vaughan - One of the best experts on this subject based on the ideXlab platform.
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beast 2 5 an advanced Software Platform for bayesian evolutionary analysis
PLOS Computational Biology, 2019Co-Authors: Remco R Bouckaert, Timothy G Vaughan, Joelle Baridosottani, Sebastian Duchene, Mathieu Fourment, Alexandra Gavryushkina, Joseph HeledAbstract:Elaboration of Bayesian phylogenetic inference methods has continued at pace in recent years with major new advances in nearly all aspects of the joint modelling of evolutionary data. It is increasingly appreciated that some evolutionary questions can only be adequately answered by combining evidence from multiple independent sources of data, including genome sequences, sampling dates, phenotypic data, radiocarbon dates, fossil occurrences, and biogeographic range information among others. Including all relevant data into a single joint model is very challenging both conceptually and computationally. Advanced computational Software packages that allow robust development of compatible (sub-)models which can be composed into a full model hierarchy have played a key role in these developments. Developing such Software frameworks is increasingly a major scientific activity in its own right, and comes with specific challenges, from practical Software design, development and engineering challenges to statistical and conceptual modelling challenges. BEAST 2 is one such computational Software Platform, and was first announced over 4 years ago. Here we describe a series of major new developments in the BEAST 2 core Platform and model hierarchy that have occurred since the first release of the Software, culminating in the recent 2.5 release.
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beast 2 a Software Platform for bayesian evolutionary analysis
PLOS Computational Biology, 2014Co-Authors: Remco R Bouckaert, Joseph Heled, Denise Kuhnert, Timothy G Vaughan, Chiehhsi Wu, Marc A Suchard, Andrew Rambaut, Alexei J DrummondAbstract:We present a new open source, extensible and flexible Software Platform for Bayesian evolutionary analysis called BEAST 2. This Software Platform is a re-design of the popular BEAST 1 Platform to correct structural deficiencies that became evident as the BEAST 1 Software evolved. Key among those deficiencies was the lack of post-deployment extensibility. BEAST 2 now has a fully developed package management system that allows third party developers to write additional functionality that can be directly installed to the BEAST 2 analysis Platform via a package manager without requiring a new Software release of the Platform. This package architecture is showcased with a number of recently published new models encompassing birth-death-sampling tree priors, phylodynamics and model averaging for substitution models and site partitioning. A second major improvement is the ability to read/write the entire state of the MCMC chain to/from disk allowing it to be easily shared between multiple instances of the BEAST Software. This facilitates checkpointing and better support for multi-processor and high-end computing extensions. Finally, the functionality in new packages can be easily added to the user interface (BEAUti 2) by a simple XML template-based mechanism because BEAST 2 has been re-designed to provide greater integration between the analysis engine and the user interface so that, for example BEAST and BEAUti use exactly the same XML file format.
Alexei J Drummond - One of the best experts on this subject based on the ideXlab platform.
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beast 2 a Software Platform for bayesian evolutionary analysis
PLOS Computational Biology, 2014Co-Authors: Remco R Bouckaert, Joseph Heled, Denise Kuhnert, Timothy G Vaughan, Chiehhsi Wu, Marc A Suchard, Andrew Rambaut, Alexei J DrummondAbstract:We present a new open source, extensible and flexible Software Platform for Bayesian evolutionary analysis called BEAST 2. This Software Platform is a re-design of the popular BEAST 1 Platform to correct structural deficiencies that became evident as the BEAST 1 Software evolved. Key among those deficiencies was the lack of post-deployment extensibility. BEAST 2 now has a fully developed package management system that allows third party developers to write additional functionality that can be directly installed to the BEAST 2 analysis Platform via a package manager without requiring a new Software release of the Platform. This package architecture is showcased with a number of recently published new models encompassing birth-death-sampling tree priors, phylodynamics and model averaging for substitution models and site partitioning. A second major improvement is the ability to read/write the entire state of the MCMC chain to/from disk allowing it to be easily shared between multiple instances of the BEAST Software. This facilitates checkpointing and better support for multi-processor and high-end computing extensions. Finally, the functionality in new packages can be easily added to the user interface (BEAUti 2) by a simple XML template-based mechanism because BEAST 2 has been re-designed to provide greater integration between the analysis engine and the user interface so that, for example BEAST and BEAUti use exactly the same XML file format.
Joelle Baridosottani - One of the best experts on this subject based on the ideXlab platform.
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beast 2 5 an advanced Software Platform for bayesian evolutionary analysis
PLOS Computational Biology, 2019Co-Authors: Remco R Bouckaert, Timothy G Vaughan, Joelle Baridosottani, Sebastian Duchene, Mathieu Fourment, Alexandra Gavryushkina, Joseph HeledAbstract:Elaboration of Bayesian phylogenetic inference methods has continued at pace in recent years with major new advances in nearly all aspects of the joint modelling of evolutionary data. It is increasingly appreciated that some evolutionary questions can only be adequately answered by combining evidence from multiple independent sources of data, including genome sequences, sampling dates, phenotypic data, radiocarbon dates, fossil occurrences, and biogeographic range information among others. Including all relevant data into a single joint model is very challenging both conceptually and computationally. Advanced computational Software packages that allow robust development of compatible (sub-)models which can be composed into a full model hierarchy have played a key role in these developments. Developing such Software frameworks is increasingly a major scientific activity in its own right, and comes with specific challenges, from practical Software design, development and engineering challenges to statistical and conceptual modelling challenges. BEAST 2 is one such computational Software Platform, and was first announced over 4 years ago. Here we describe a series of major new developments in the BEAST 2 core Platform and model hierarchy that have occurred since the first release of the Software, culminating in the recent 2.5 release.
