342 research outputs found
Make the most of your samples : Bayes factor estimators for high-dimensional models of sequence evolution
Background: Accurate model comparison requires extensive computation times, especially for parameter-rich models of sequence evolution. In the Bayesian framework, model selection is typically performed through the evaluation of a Bayes factor, the ratio of two marginal likelihoods (one for each model). Recently introduced techniques to estimate (log) marginal likelihoods, such as path sampling and stepping-stone sampling, offer increased accuracy over the traditional harmonic mean estimator at an increased computational cost. Most often, each model's marginal likelihood will be estimated individually, which leads the resulting Bayes factor to suffer from errors associated with each of these independent estimation processes.
Results: We here assess the original 'model-switch' path sampling approach for direct Bayes factor estimation in phylogenetics, as well as an extension that uses more samples, to construct a direct path between two competing models, thereby eliminating the need to calculate each model's marginal likelihood independently. Further, we provide a competing Bayes factor estimator using an adaptation of the recently introduced stepping-stone sampling algorithm and set out to determine appropriate settings for accurately calculating such Bayes factors, with context-dependent evolutionary models as an example. While we show that modest efforts are required to roughly identify the increase in model fit, only drastically increased computation times ensure the accuracy needed to detect more subtle details of the evolutionary process.
Conclusions: We show that our adaptation of stepping-stone sampling for direct Bayes factor calculation outperforms the original path sampling approach as well as an extension that exploits more samples. Our proposed approach for Bayes factor estimation also has preferable statistical properties over the use of individual marginal likelihood estimates for both models under comparison. Assuming a sigmoid function to determine the path between two competing models, we provide evidence that a single well-chosen sigmoid shape value requires less computational efforts in order to approximate the true value of the (log) Bayes factor compared to the original approach. We show that the (log) Bayes factors calculated using path sampling and stepping-stone sampling differ drastically from those estimated using either of the harmonic mean estimators, supporting earlier claims that the latter systematically overestimate the performance of high-dimensional models, which we show can lead to erroneous conclusions. Based on our results, we argue that highly accurate estimation of differences in model fit for high-dimensional models requires much more computational effort than suggested in recent studies on marginal likelihood estimation
Ï€BUSS:a parallel BEAST/BEAGLE utility for sequence simulation under complex evolutionary scenarios
Background: Simulated nucleotide or amino acid sequences are frequently used
to assess the performance of phylogenetic reconstruction methods. BEAST, a
Bayesian statistical framework that focuses on reconstructing time-calibrated
molecular evolutionary processes, supports a wide array of evolutionary models,
but lacked matching machinery for simulation of character evolution along
phylogenies.
Results: We present a flexible Monte Carlo simulation tool, called piBUSS,
that employs the BEAGLE high performance library for phylogenetic computations
within BEAST to rapidly generate large sequence alignments under complex
evolutionary models. piBUSS sports a user-friendly graphical user interface
(GUI) that allows combining a rich array of models across an arbitrary number
of partitions. A command-line interface mirrors the options available through
the GUI and facilitates scripting in large-scale simulation studies. Analogous
to BEAST model and analysis setup, more advanced simulation options are
supported through an extensible markup language (XML) specification, which in
addition to generating sequence output, also allows users to combine simulation
and analysis in a single BEAST run.
Conclusions: piBUSS offers a unique combination of flexibility and
ease-of-use for sequence simulation under realistic evolutionary scenarios.
Through different interfaces, piBUSS supports simulation studies ranging from
modest endeavors for illustrative purposes to complex and large-scale
assessments of evolutionary inference procedures. The software aims at
implementing new models and data types that are continuously being developed as
part of BEAST/BEAGLE.Comment: 13 pages, 2 figures, 1 tabl
Preliminary structural and chemical study of two quartzite varieties from the same geological formation : a first step in the sourcing of quartzites utilized during the Mesolithic in northwest Europe
Adaptive MCMC in Bayesian phylogenetics: an application to analyzing partitioned data in BEAST
Advances in sequencing technology continue to deliver increasingly large molecular sequence datasets that are often heavily partitioned in order to accurately model the underlying evolutionary processes. In phylogenetic analyses, partitioning strategies involve estimating conditionally independent models of molecular evolution for different genes and different positions within those genes, requiring a large number of evolutionary parameters that have to be estimated, leading to an increased computational burden for such analyses. The past two decades have also seen the rise of multi-core processors, both in the central processing unit (CPU) and Graphics processing unit processor markets, enabling massively parallel computations that are not yet fully exploited by many software packages for multipartite analyses.status: publishe
Online Bayesian phylodynamic inference in BEAST with application to epidemic reconstruction
Reconstructing pathogen dynamics from genetic data as they become available
during an outbreak or epidemic represents an important statistical scenario in
which observations arrive sequentially in time and one is interested in
performing inference in an 'online' fashion. Widely-used Bayesian phylogenetic
inference packages are not set up for this purpose, generally requiring one to
recompute trees and evolutionary model parameters de novo when new data arrive.
To accommodate increasing data flow in a Bayesian phylogenetic framework, we
introduce a methodology to efficiently update the posterior distribution with
newly available genetic data. Our procedure is implemented in the BEAST 1.10
software package, and relies on a distance-based measure to insert new taxa
into the current estimate of the phylogeny and imputes plausible values for new
model parameters to accommodate growing dimensionality. This augmentation
creates informed starting values and re-uses optimally tuned transition kernels
for posterior exploration of growing data sets, reducing the time necessary to
converge to target posterior distributions. We apply our framework to data from
the recent West African Ebola virus epidemic and demonstrate a considerable
reduction in time required to obtain posterior estimates at different time
points of the outbreak. Beyond epidemic monitoring, this framework easily finds
other applications within the phylogenetics community, where changes in the
data -- in terms of alignment changes, sequence addition or removal -- present
common scenarios that can benefit from online inference.Comment: 20 pages, 3 figure
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