Markov chain Monte Carlo ≈ Markov chain Monte Carlo
View article: Posterior Summarization in Bayesian Phylogenetics Using Tracer 1.7
Posterior Summarization in Bayesian Phylogenetics Using Tracer 1.7 Open
Bayesian inference of phylogeny using Markov chain Monte Carlo (MCMC) plays a central role in understanding evolutionary history from molecular sequence data. Visualizing and analyzing the MCMC-generated samples from the posterior distribu…
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<i>Stan</i>: A Probabilistic Programming Language Open
Stan is a probabilistic programming language for specifying statistical models. A Stan program imperatively defines a log probability function over parameters conditioned on specified data and constants. As of version 2.14.0, Stan provides…
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Bayesian phylogenetic and phylodynamic data integration using BEAST 1.10 Open
The Bayesian Evolutionary Analysis by Sampling Trees (BEAST) software package has become a primary tool for Bayesian phylogenetic and phylodynamic inference from genetic sequence data. BEAST unifies molecular phylogenetic reconstruction wi…
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Probabilistic programming in Python using PyMC3 Open
Probabilistic programming allows for automatic Bayesian inference on user-defined probabilistic models. Recent advances in Markov chain Monte Carlo (MCMC) sampling allow inference on increasingly complex models. This class of MCMC, known a…
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Variational Inference: A Review for Statisticians Open
One of the core problems of modern statistics is to approximate difficult-to-compute probability densities. This problem is especially important in Bayesian statistics, which frames all inference about unknown quantities as a calculation i…
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dynesty: a dynamic nested sampling package for estimating Bayesian posteriors and evidences Open
We present dynesty, a public, open-source, python package to estimate Bayesian posteriors and evidences (marginal likelihoods) using the dynamic nested sampling methods developed by Higson et al. By adaptively allocating samples based on p…
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Importance Nested Sampling and the MultiNest Algorithm Open
Bayesian inference involves two main computational challenges. First, in\nestimating the parameters of some model for the data, the posterior\ndistribution may well be highly multi-modal: a regime in which the convergence\nto stationarity …
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Multi-rate Poisson tree processes for single-locus species delimitation under maximum likelihood and Markov chain Monte Carlo Open
Motivation In recent years, molecular species delimitation has become a routine approach for quantifying and classifying biodiversity. Barcoding methods are of particular importance in large-scale surveys as they promote fast species disco…
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bModelTest: Bayesian phylogenetic site model averaging and model comparison Open
With the new method the site model can be inferred (and marginalized) during the MCMC analysis and does not need to be pre-determined, as is now often the case in practice, by likelihood-based methods. The method is implemented in the bMod…
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<b>runjags</b>: An<i>R</i>Package Providing Interface Utilities, Model Templates, Parallel Computing Methods and Additional Distributions for MCMC Models in<b>JAGS</b> Open
The runjags package provides a set of interface functions to facilitate running Markov chain Monte Carlo models in JAGS from within R. Automated calculation of appropriate convergence and sample length diagnostics, user-friendly access to …
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GetDist: a Python package for analysing Monte Carlo samples Open
Monte Carlo techniques, including MCMC and other methods, are widely used in Bayesian inference to generate sets of samples from a parameter space of interest. The Python GetDist package provides tools for analysing these samples and calcu…
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RadVel: The Radial Velocity Modeling Toolkit Open
RadVel is an open-source Python package for modeling Keplerian orbits in radial velocity (RV) timeseries. RadVel provides a convenient framework to fit RVs using maximum a posteriori optimization and to compute robust confidence intervals …
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Network meta-analysis: application and practice using R software Open
The objective of this study is to describe the general approaches to network meta-analysis that are available for quantitative data synthesis using R software. We conducted a network meta-analysis using two approaches: Bayesian and frequen…
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Species Tree Inference with BPP Using Genomic Sequences and the Multispecies Coalescent Open
The multispecies coalescent provides a natural framework for accommodating ancestral genetic polymorphism and coalescent processes that can cause different genomic regions to have different genealogical histories. The Bayesian program BPP …
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Modeling zero-inflated count data with glmmTMB Open
Ecological phenomena are often measured in the form of count data. These data can be analyzed using generalized linear mixed models (GLMMs) when observations are correlated in ways that require random effects. However, count data are often…
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The geometric foundations of Hamiltonian Monte Carlo Open
Although Hamiltonian Monte Carlo has proven an empirical success, the lack of a rigorous theoretical understanding of the algorithm has in many ways impeded both principled developments of the method and use of the algorithm in practice. I…
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An introduction to Bayesian inference in gravitational-wave astronomy: Parameter estimation, model selection, and hierarchical models Open
This is an introduction to Bayesian inference with a focus on hierarchical models and hyper-parameters. We write primarily for an audience of Bayesian novices, but we hope to provide useful insights for seasoned veterans as well. Examples …
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Rapid Bayesian position reconstruction for gravitational-wave transients Open
Within the next few years, Advanced LIGO and Virgo should detect gravitational waves from binary neutron star and neutron star-black hole mergers. These sources are also predicted to power a broad array of electromagnetic transients. Becau…
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Multivariate <span>C</span>opula <span>A</span>nalysis <span>T</span>oolbox (MvCAT): Describing dependence and underlying uncertainty using a <span>B</span>ayesian framework Open
We present a newly developed Multivariate Copula Analysis Toolbox (MvCAT) which includes a wide range of copula families with different levels of complexity. MvCAT employs a Bayesian framework with a residual‐based Gaussian likelihood func…
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Inversion of Surface Deformation Data for Rapid Estimates of Source Parameters and Uncertainties: A Bayesian Approach Open
New satellite missions (e.g., the European Space Agency's Sentinel‐1 constellation), advances in data downlinking, and rapid product generation now provide us with the ability to access space‐geodetic data within hours of their acquisition…
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<span>tRophicPosition</span>, an<span>r</span>package for the Bayesian estimation of trophic position from consumer stable isotope ratios Open
Stable isotope analysis provides a powerful tool to identify the energy sources which fuel consumers, to understand trophic interactions and to infer consumer trophic position (TP), an important concept that describes the ecological role o…
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Data Analysis Recipes: Using Markov Chain Monte Carlo* Open
Markov Chain Monte Carlo (MCMC) methods for sampling probability density functions (combined with abundant computational resources) have transformed the sciences, especially in performing probabilistic inferences, or fitting models to data…
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A Practical Guide to Surface Kinetic Monte Carlo Simulations Open
This review article is intended as a practical guide for newcomers to the field of kinetic Monte Carlo (KMC) simulations, and specifically to lattice KMC simulations as prevalently used for surface and interface applications. We will provi…
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Genomic infectious disease epidemiology in partially sampled and ongoing outbreaks Open
Genomic data are increasingly being used to understand infectious disease epidemiology. Isolates from a given outbreak are sequenced, and the patterns of shared variation are used to infer which isolates within the outbreak are most closel…
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Faster estimation of Bayesian models in ecology using Hamiltonian Monte Carlo Open
Summary Bayesian inference is a powerful tool to better understand ecological processes across varied subfields in ecology, and is often implemented in generic and flexible software packages such as the widely used BUGS family (BUGS, WinBU…
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Deep Autoregressive Models for the Efficient Variational Simulation of Many-Body Quantum Systems Open
Artificial neural networks were recently shown to be an efficient representation of highly entangled many-body quantum states. In practical applications, neural-network states inherit numerical schemes used in variational Monte Carlo metho…
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Is BAMM Flawed? Theoretical and Practical Concerns in the Analysis of Multi-Rate Diversification Models Open
Bayesian analysis of macroevolutionary mixtures (BAMM) is a statistical framework that uses reversible jump Markov chain Monte Carlo to infer complex macroevolutionary dynamics of diversification and phenotypic evolution on phylogenetic tr…
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RWTY (R We There Yet): An R package for examining convergence of Bayesian phylogenetic analyses Open
Bayesian inference using Markov chain Monte Carlo (MCMC) has become one of the primary methods used to infer phylogenies from sequence data. Assessing convergence is a crucial component of these analyses, as it establishes the reliability …
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Ripple effect modelling of supplier disruption: integrated Markov chain and dynamic Bayesian network approach Open
The ripple effect can occur when a supplier base disruption cannot be localised and consequently propagates downstream the supply chain (SC), adversely affecting performance. While stress-testing of SC designs and assessment of their vulne…
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Mixture Models With a Prior on the Number of Components Open
A natural Bayesian approach for mixture models with an unknown number of components is to take the usual finite mixture model with symmetric Dirichlet weights, and put a prior on the number of components—that is, to use a mixture of finite…