Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Motivation: Gene genealogies offer a porful context for inferences about the evolutionary process based on presently segregating DNA variation. In many cases, it is the distribution of population parameters, marginalized over the effectively infinite-dimensional tree space, that is of interest. Our evolutionary forest (EF) algorithm uses Monte Carlo methods to generate posterior distributions of population parameters. A novel feature is the updating of parameter values based on a probability measure defined on an ensemble of histories (a forest of genealogies), rather than a single tree.#R##N##R##N#Results: The EF algorithm generates samples from the correct marginal distribution of population parameters. Applied to actual data from closely related fruit fly species, it rapidly converged to posterior distributions that closely approximated the exact posteriors generated through massive computational effort. Applied to simulated data, it generated credible intervals that covered the actual parameter values in accordance with the nominal probabilities.#R##N##R##N#Availability: A C++ implementation of this method is freely accessible at http://www.isds.duke.edu/~scl13#R##N##R##N#Contact: scotland@stat.duke.edu
['Scotland C. Leman', 'Marcy K. Uyenoyama', 'Michael Lavine', 'Yuguo Chen']
I am writing a research paper on a idea The evolutionary forest algorithm
[{"from": "human", "value": "I am writing a research paper on a idea The evolutionary forest algorithm"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Motivation: Gene genealogies offer a porful context for inferences about the evolutionary process based on presently segregating DNA variation. In many cases, it is the distribution of population parameters, marginalized over the effectively infinite-dimensional tree space, that is of interest. Our evolutionary forest (EF) algorithm uses Monte Carlo methods to generate posterior distributions of population parameters. A novel feature is the updating of parameter values based on a probability measure defined on an ensemble of histories (a forest of genealogies), rather than a single tree.#R##N##R##N#Results: The EF algorithm generates samples from the correct marginal distribution of population parameters. Applied to actual data from closely related fruit fly species, it rapidly converged to posterior distributions that closely approximated the exact posteriors generated through massive computational effort. Applied to simulated data, it generated credible intervals that covered the actual parameter values in accordance with the nominal probabilities.#R##N##R##N#Availability: A C++ implementation of this method is freely accessible at http://www.isds.duke.edu/~scl13#R##N##R##N#Contact: scotland@stat.duke.edu"}]