An efficient Bayesian inference framework for coalescent-based nonparametric phylodynamics

An efficient Bayesian inference framework for coalescent-based nonparametric phylodynamics

MOTIVATION 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...

Supplementary to An Efficient Bayesian Inference Framework for Coalescent-Based Nonparametric Phylodynamics

1 SplitHMC Here, we show how to solve Hamiltonian dynamics defined by (13) using the " splitting " strategy. Denote

Bayesian Nonparametric and Parametric Inference

This paper reviews Bayesian Nonparametric methods and discusses how parametric predictive densities can be constructed using nonparametric ideas.

Efficient Bayesian inference under the structured coalescent

MOTIVATION Population structure significantly affects evolutionary dynamics. Such structure may be due to spatial segregation, but may also reflect any other gene-flow-limiting aspect of a model. In combination with the structured coalescent, this fact can be used to inform phylogenetic tree reconstruction, as well as to infer parameters such as migration rates and subpopulation sizes from anno...

Efficient Nonparametric Importance Sampling for Bayesian Inference