Posterior probability ≈ Posterior probability
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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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Fast Kd-Trees for the Kullback-Leibler Divergence and Other Decomposable Bregman Divergences Open
The contributions of the paper span theoretical and implementational results. First, we prove that Kd-trees can be extended to ℝ^d with the distance measured by an arbitrary Bregman divergence. Perhaps surprisingly, this shows that the tri…
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A Simple New Approach to Variable Selection in Regression, with Application to Genetic Fine Mapping Open
Summary We introduce a simple new approach to variable selection in linear regression, with a particular focus on quantifying uncertainty in which variables should be selected. The approach is based on a new model—the ‘sum of single effect…
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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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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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Rejecting or Accepting Parameter Values in Bayesian Estimation Open
This article explains a decision rule that uses Bayesian posterior distributions as the basis for accepting or rejecting null values of parameters. This decision rule focuses on the range of plausible values indicated by the highest densit…
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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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Crustal and uppermost mantle structure beneath the United States Open
This paper presents a new model of the shear velocity structure of the crust and uppermost mantle beneath the contiguous U.S. The model is based on more than a decade of USArray Transportable Array (TA) data across the U.S. and derives fro…
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Importance weighted autoencoders Open
The variational autoencoder (VAE; Kingma, Welling (2014)) is a recently proposed generative model pairing a top-down generative network with a bottom-up recognition network which approximates posterior inference. It typically makes strong …
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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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Multiplicative Normalizing Flows for Variational Bayesian Neural Networks Open
We reinterpret multiplicative noise in neural networks as auxiliary random variables that augment the approximate posterior in a variational setting for Bayesian neural networks. We show that through this interpretation it is both efficien…
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Variational Denoising Network: Toward Blind Noise Modeling and Removal Open
Blind image denoising is an important yet very challenging problem in computer vision due to the complicated acquisition process of real images. In this work we propose a new variational inference method, which integrates both noise estima…
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Model Selection and Parameter Inference in Phylogenetics Using Nested Sampling Open
Bayesian inference methods rely on numerical algorithms for both model selection and parameter inference. In general, these algorithms require a high computational effort to yield reliable estimates. One of the major challenges in phylogen…
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Analysis of iterative ensemble smoothers for solving inverse problems Open
This paper examines the properties of the Iterated Ensemble Smoother (IES) and the Multiple Data Assimilation Ensemble Smoother (ES–MDA) for solving the history matching problem. The iterative methods are compared with the standard Ensembl…
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Frequentist Consistency of Variational Bayes Open
A key challenge for modern Bayesian statistics is how to perform scalable inference of posterior distributions. To address this challenge, variational Bayes (VB) methods have emerged as a popular alternative to the classical Markov chain M…
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<i>refnx</i>: neutron and X-ray reflectometry analysis in Python Open
refnx is a model-based neutron and X-ray reflectometry data analysis package written in Python. It is cross platform and has been tested on Linux, macOS and Windows. Its graphical user interface is browser based, through a Jupyter notebook…
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Polygenic Prediction via Bayesian Regression and Continuous Shrinkage Priors Open
Polygenic prediction has shown promise in identifying individuals at high risk for complex diseases, and may become clinically useful as the predictive performance of polygenic risk scores (PRS) improves. Here, we present PRS-CS, a novel p…
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Transitional Markov Chain Monte Carlo: Observations and Improvements Open
The Transitional Markov chain Monte Carlo (TMCMC) method is a widely used method for Bayesian updating and Bayesian model class selection. The method is based on successively sampling from a sequence of distributions that gradually approac…
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Fast $ε$-free Inference of Simulation Models with Bayesian Conditional Density Estimation Open
Many statistical models can be simulated forwards but have intractable likelihoods. Approximate Bayesian Computation (ABC) methods are used to infer properties of these models from data. Traditionally these methods approximate the posterio…
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Estimation of the Continuous Ranked Probability Score with Limited Information and Applications to Ensemble Weather Forecasts Open
International audience
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Bayesian truncation errors in chiral effective field theory: Nucleon-nucleon observables Open
Chiral effective field theory (EFT) predictions are necessarily truncated at\nsome order in the EFT expansion, which induces an error that must be quantified\nfor robust statistical comparisons to experiment. In previous work, a Bayesian\n…
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Gravitational-wave parameter estimation with autoregressive neural network flows Open
We introduce the use of autoregressive normalizing flows for rapid\nlikelihood-free inference of binary black hole system parameters from\ngravitational-wave data with deep neural networks. A normalizing flow is an\ninvertible mapping on a…
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Priors and Posteriors in Bayesian Timing of Divergence Analyses: The Age of Butterflies Revisited Open
The need for robust estimates of times of divergence is essential for downstream analyses, yet assessing this robustness is still rare. We generated a time-calibrated genus-level phylogeny of butterflies (Papilionoidea), including 994 taxa…
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Probabilistic neural networks for fluid flow surrogate modeling and data recovery Open
We consider the use of probabilistic neural networks for fluid flow surrogate modeling and data recovery. This framework is constructed by assuming that the target variables are sampled from a Gaussian distribution conditioned on the input…
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SiCloneFit: Bayesian inference of population structure, genotype, and phylogeny of tumor clones from single-cell genome sequencing data Open
Accumulation and selection of somatic mutations in a Darwinian framework result in intra-tumor heterogeneity (ITH) that poses significant challenges to the diagnosis and clinical therapy of cancer. Identification of the tumor cell populati…
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Distributed Fusion With Multi-Bernoulli Filter Based on Generalized Covariance Intersection Open
In this paper, we propose a distributed multi-object tracking algorithm\nthrough the use of multi-Bernoulli (MB) filter based on generalized Covariance\nIntersection (G-CI). Our analyses show that the G-CI fusion with two MB\nposterior dis…
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Time‐varying nonstationary multivariate risk analysis using a dynamic Bayesian copula Open
A time‐varying risk analysis is proposed for an adaptive design framework in nonstationary conditions arising from climate change. A Bayesian, dynamic conditional copula is developed for modeling the time‐varying dependence structure betwe…
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HELP: xid+, the probabilistic de-blender for<i>Herschel</i>SPIRE maps Open
We have developed a new prior-based source extraction tool, xid+, to carry out photometry in the Herschel SPIRE (Spectral and Photometric Imaging Receiver) maps at the positions of known sources. xid+ is developed using a probabilistic Bay…
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Where Do Priors Come From? Applying Guidelines to Construct Informative Priors in Small Sample Research Open
This article demonstrates the usefulness of Bayesian estimation with small samples. In Bayesian estimation, prior information can be included, which increases the precision of the posterior distribution. The posterior distribution reflects…