@article {1231986,
	title = {Bayesian Estimation of Population Size Changes by Sampling Tajima{\textquoteright}s Trees},
	journal = {Genetics},
	volume = {213},
	year = {2019},
	pages = {967-986},
	abstract = {The large state space of gene genealogies is a major hurdle for inference methods based on Kingman{\textquoteright}s coalescent. Here, we present a new Bayesian approach for inferring past population sizes, which relies on a lower-resolution coalescent process that we refer to as {\textquotedblleft}Tajima{\textquoteright}s coalescent.{\textquotedblright} Tajima{\textquoteright}s coalescent has a drastically smaller state space, and hence it is a computationally more efficient model, than the standard Kingman coalescent. We provide a new algorithm for efficient and exact likelihood calculations for data without recombination, which exploits a directed acyclic graph and a correspondingly tailored Markov Chain Monte Carlo method. We compare the performance of our Bayesian Estimation of population size changes by Sampling Tajima{\textquoteright}s Trees (BESTT) with a popular implementation of coalescent-based inference in BEAST using simulated and human data. We empirically demonstrate that BESTT can accurately infer effective population sizes, and it further provides an efficient alternative to the Kingman{\textquoteright}s coalescent. The algorithms described here are implemented in the R package phylodyn, which is available for download at https://github.com/JuliaPalacios/phylodyn.},
	author = {Palacios, Julia A. and Veber, Amandine and Cappello, Lorenzo and Wang, Zhangyuan and Wakeley, John and Ramachandra, Sohini}
}
