title: An efficient Bayesian inference framework for coalescent-based nonparametric phylodynamics.
abstract: BACKGROUND
  The field of phylodynamics focuses on the problem of reconstructing population size dynamics over time using current genetic samples taken from the population of interest. This technique has been extensively used in many areas of biology but is particularly useful for studying the spread of quickly evolving infectious diseases agents, e.g. influenza virus. Phylodynamic inference uses a coalescent model that defines a probability density for the genealogy of randomly sampled individuals from the population. When we assume that such a genealogy is known, the coalescent model, equipped with a Gaussian process prior on population size trajectory, allows for nonparametric Bayesian estimation of population size dynamics. Although this approach is quite powerful, large datasets collected during infectious disease surveillance challenge the state-of-the-art of Bayesian phylodynamics and demand inferential methods with relatively low computational cost.
  
  
  RESULTS
  To satisfy this demand, we provide a computationally efficient Bayesian inference framework based on Hamiltonian Monte Carlo for coalescent process models. Moreover, we show that by splitting the Hamiltonian function, we can further improve the efficiency of this approach. Using several simulated and real datasets, we show that our method provides accurate estimates of population size dynamics and is substantially faster than alternative methods based on elliptical slice sampler and Metropolis-adjusted Langevin algorithm.
  
  
  METHODS
  The R code for all simulation studies and real data analysis conducted in this article are publicly available at http://www.ics.uci.edu/∼slan/lanzi/CODES.html and in the R package phylodyn available at https://github.com/mdkarcher/phylodyn.
  
  
  BACKGROUND
  S.Lan@warwick.ac.uk or babaks@uci.edu
  
  
  BACKGROUND
  Supplementary data are available at Bioinformatics online.
authors.affiliation: Department of Statistics, University of Warwick, Coventry CV4 7AL, UK.
authors.forename: Shiwei
authors.initials: S
authors.lastname: Lan
authors.affiliation: Department of Organismic and Evolutionary Biology, Harvard University, MA 02138, US, Department of Ecology and Evolutionary Biology, Brown University, RI 02912, US, Center for Computational Molecular Biology, Brown University.
authors.forename: Julia A
authors.initials: JA
authors.lastname: Palacios
authors.affiliation: Department of Statistics, University of Washington, WA 98195, US.
authors.forename: Michael
authors.initials: M
authors.lastname: Karcher
authors.affiliation: Department of Statistics, University of Washington, WA 98195, US, Department of Biology, University of Washington and.
authors.forename: Vladimir N
authors.initials: VN
authors.lastname: Minin
authors.affiliation: Department of Statistics, University of California, Irvine, CA 92697, US.
authors.forename: Babak
authors.initials: B
authors.lastname: Shahbaba
mesh_terms: D000465:Algorithms; D001499:Bayes Theorem; D005828:Genetics, Population / Q000379:methods*; D006801:Humans; D007251:Influenza, Human / Q000453:epidemiology; D015233:Models, Statistical; D009010:Monte Carlo Method; D009975:Orthomyxoviridae / Q000235:genetics; D011156:Population Density; D011157:Population Dynamics; D012984:Software; D018709:Statistics, Nonparametric