Uncertainty in Artificial Intelligence
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Integrated Nested Laplace Approximation for Bayesian Nonparametric Phylodynamics
Julia Palacios, Vladimir Minin
Abstract:
The goal of phylodynamics, an area on the intersection of phylogenetics and population genetics, is to reconstruct population size dynamics from genetic data. Recently, a series of nonparametric Bayesian methods have been proposed for such demographic reconstructions. These methods rely on prior specifications based on Gaussian processes and proceed by approximating the posterior distribution of population size trajectories via Markov chain Monte Carlo (MCMC) methods. In this paper, we adapt an integrated nested Laplace approximation (INLA), a recently proposed approximate Bayesian inference for latent Gaussian models, to the estimation of population size trajectories. We show that when a genealogy of sampled individuals can be reliably estimated from genetic data, INLA enjoys high accuracy and can replace MCMC entirely. We demonstrate significant computational efficiency over the state-of-the-art MCMC methods. We illustrate INLA-based population size inference using simulations and genealogies of hepatitis C and human influenza viruses.
Keywords:
Pages: 726-735
PS Link:
PDF Link: /papers/12/p726-palacios.pdf
BibTex:
@INPROCEEDINGS{Palacios12,
AUTHOR = "Julia Palacios and Vladimir Minin",
TITLE = "Integrated Nested Laplace Approximation for Bayesian Nonparametric Phylodynamics",
BOOKTITLE = "Proceedings of the Twenty-Eighth Conference Annual Conference on Uncertainty in Artificial Intelligence (UAI-12)",
PUBLISHER = "AUAI Press",
ADDRESS = "Corvallis, Oregon",
YEAR = "2012",
PAGES = "726--735"
}


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