The Experts below are selected from a list of 111840 Experts worldwide ranked by ideXlab platform
Evan Sinukoff - One of the best experts on this subject based on the ideXlab platform.
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radvel the radial velocity modeling toolkit
Publications of the Astronomical Society of the Pacific, 2018Co-Authors: Benjamin J Fulton, Erik A Petigura, Sarah Blunt, Evan SinukoffAbstract:RadVel is an open-source Python package for modeling Keplerian orbits in radial velocity (RV) timeseries. RadVel provides a convenient framework to fit RVs using maximum a Posteriori optimization and to compute robust confidence intervals by sampling the posterior probability density via Markov Chain Monte Carlo (MCMC). RadVel allows users to float or fix parameters, impose priors, and perform Bayesian model comparison. We have implemented real-time MCMC convergence tests to ensure adequate sampling of the posterior. RadVel can output a number of publication-quality plots and tables. Users may interface with RadVel through a convenient command-line interface or directly from Python. The code is object-oriented and thus naturally extensible. We encourage contributions from the community. Documentation is available at http://radvel.readthedocs.io.
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radvel the radial velocity modeling toolkit
arXiv: Instrumentation and Methods for Astrophysics, 2018Co-Authors: Benjamin J Fulton, Erik A Petigura, Sarah Blunt, Evan SinukoffAbstract:RadVel is an open source Python package for modeling Keplerian orbits in radial velocity (RV) time series. RadVel provides a convenient framework to fit RVs using maximum a Posteriori optimization and to compute robust confidence intervals by sampling the posterior probability density via Markov Chain Monte Carlo (MCMC). RadVel allows users to float or fix parameters, impose priors, and perform Bayesian model comparison. We have implemented realtime MCMC convergence tests to ensure adequate sampling of the posterior. RadVel can output a number of publication-quality plots and tables. Users may interface with RadVel through a convenient command-line interface or directly from Python. The code is object-oriented and thus naturally extensible. We encourage contributions from the community. Documentation is available at this http URL
Benjamin J Fulton - One of the best experts on this subject based on the ideXlab platform.
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radvel the radial velocity modeling toolkit
Publications of the Astronomical Society of the Pacific, 2018Co-Authors: Benjamin J Fulton, Erik A Petigura, Sarah Blunt, Evan SinukoffAbstract:RadVel is an open-source Python package for modeling Keplerian orbits in radial velocity (RV) timeseries. RadVel provides a convenient framework to fit RVs using maximum a Posteriori optimization and to compute robust confidence intervals by sampling the posterior probability density via Markov Chain Monte Carlo (MCMC). RadVel allows users to float or fix parameters, impose priors, and perform Bayesian model comparison. We have implemented real-time MCMC convergence tests to ensure adequate sampling of the posterior. RadVel can output a number of publication-quality plots and tables. Users may interface with RadVel through a convenient command-line interface or directly from Python. The code is object-oriented and thus naturally extensible. We encourage contributions from the community. Documentation is available at http://radvel.readthedocs.io.
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radvel the radial velocity modeling toolkit
arXiv: Instrumentation and Methods for Astrophysics, 2018Co-Authors: Benjamin J Fulton, Erik A Petigura, Sarah Blunt, Evan SinukoffAbstract:RadVel is an open source Python package for modeling Keplerian orbits in radial velocity (RV) time series. RadVel provides a convenient framework to fit RVs using maximum a Posteriori optimization and to compute robust confidence intervals by sampling the posterior probability density via Markov Chain Monte Carlo (MCMC). RadVel allows users to float or fix parameters, impose priors, and perform Bayesian model comparison. We have implemented realtime MCMC convergence tests to ensure adequate sampling of the posterior. RadVel can output a number of publication-quality plots and tables. Users may interface with RadVel through a convenient command-line interface or directly from Python. The code is object-oriented and thus naturally extensible. We encourage contributions from the community. Documentation is available at this http URL
Ward C Wheeler - One of the best experts on this subject based on the ideXlab platform.
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maximum a Posteriori probability assignment map a an optimality criterion for phylogenetic trees via weighting and dynamic programming
Cladistics, 2014Co-Authors: Ward C WheelerAbstract:One of the most time-consuming aspects of Bayesian posterior probability analysis in the analysis of phylogenetic trees is the use of Metropolis-coupled Markov chain Monte Carlo (MC3) methods to determine relative posteriors and identify maximum a Posteriori (MAP) trees. Here, analytical and numerical methods are presented to determine tree likelihoods that are integrated over edge-length (and other parameter) distributions. Given topological (tree) priors (flat or otherwise), this allows for identification of the maximum posterior probability assignment (MAP-A) of character states to non-leaf tree vertices via dynamic programming. Using this form of posterior probability as an optimality criterion, tree space can be searched using standard trajectory techniques and heuristically optimal MAP-A trees can be identified with considerable time savings over MC3. Example cases are presented using aligned and unaligned molecular sequences as well as combined molecular and anatomical data.
Sarah Blunt - One of the best experts on this subject based on the ideXlab platform.
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radvel the radial velocity modeling toolkit
Publications of the Astronomical Society of the Pacific, 2018Co-Authors: Benjamin J Fulton, Erik A Petigura, Sarah Blunt, Evan SinukoffAbstract:RadVel is an open-source Python package for modeling Keplerian orbits in radial velocity (RV) timeseries. RadVel provides a convenient framework to fit RVs using maximum a Posteriori optimization and to compute robust confidence intervals by sampling the posterior probability density via Markov Chain Monte Carlo (MCMC). RadVel allows users to float or fix parameters, impose priors, and perform Bayesian model comparison. We have implemented real-time MCMC convergence tests to ensure adequate sampling of the posterior. RadVel can output a number of publication-quality plots and tables. Users may interface with RadVel through a convenient command-line interface or directly from Python. The code is object-oriented and thus naturally extensible. We encourage contributions from the community. Documentation is available at http://radvel.readthedocs.io.
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radvel the radial velocity modeling toolkit
arXiv: Instrumentation and Methods for Astrophysics, 2018Co-Authors: Benjamin J Fulton, Erik A Petigura, Sarah Blunt, Evan SinukoffAbstract:RadVel is an open source Python package for modeling Keplerian orbits in radial velocity (RV) time series. RadVel provides a convenient framework to fit RVs using maximum a Posteriori optimization and to compute robust confidence intervals by sampling the posterior probability density via Markov Chain Monte Carlo (MCMC). RadVel allows users to float or fix parameters, impose priors, and perform Bayesian model comparison. We have implemented realtime MCMC convergence tests to ensure adequate sampling of the posterior. RadVel can output a number of publication-quality plots and tables. Users may interface with RadVel through a convenient command-line interface or directly from Python. The code is object-oriented and thus naturally extensible. We encourage contributions from the community. Documentation is available at this http URL
Erik A Petigura - One of the best experts on this subject based on the ideXlab platform.
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radvel the radial velocity modeling toolkit
Publications of the Astronomical Society of the Pacific, 2018Co-Authors: Benjamin J Fulton, Erik A Petigura, Sarah Blunt, Evan SinukoffAbstract:RadVel is an open-source Python package for modeling Keplerian orbits in radial velocity (RV) timeseries. RadVel provides a convenient framework to fit RVs using maximum a Posteriori optimization and to compute robust confidence intervals by sampling the posterior probability density via Markov Chain Monte Carlo (MCMC). RadVel allows users to float or fix parameters, impose priors, and perform Bayesian model comparison. We have implemented real-time MCMC convergence tests to ensure adequate sampling of the posterior. RadVel can output a number of publication-quality plots and tables. Users may interface with RadVel through a convenient command-line interface or directly from Python. The code is object-oriented and thus naturally extensible. We encourage contributions from the community. Documentation is available at http://radvel.readthedocs.io.
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radvel the radial velocity modeling toolkit
arXiv: Instrumentation and Methods for Astrophysics, 2018Co-Authors: Benjamin J Fulton, Erik A Petigura, Sarah Blunt, Evan SinukoffAbstract:RadVel is an open source Python package for modeling Keplerian orbits in radial velocity (RV) time series. RadVel provides a convenient framework to fit RVs using maximum a Posteriori optimization and to compute robust confidence intervals by sampling the posterior probability density via Markov Chain Monte Carlo (MCMC). RadVel allows users to float or fix parameters, impose priors, and perform Bayesian model comparison. We have implemented realtime MCMC convergence tests to ensure adequate sampling of the posterior. RadVel can output a number of publication-quality plots and tables. Users may interface with RadVel through a convenient command-line interface or directly from Python. The code is object-oriented and thus naturally extensible. We encourage contributions from the community. Documentation is available at this http URL
