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Author(s):  
Carolina Barata ◽  
Rui Borges ◽  
Carolin Kosiol

For over a decade, experimental evolution has been combined with high-throughput sequencing techniques in so-called Evolve-and-Resequence (E&R) experiments. This allows testing for selection in populations kept in the laboratory under given experimental conditions. However, identifying signatures of adaptation in E&R datasets is far from trivial, and it is still necessary to develop more efficient and statistically sound methods for detecting selection in genome-wide data. Here, we present Bait-ER - a fully Bayesian approach based on the Moran model of allele evolution to estimate selection coefficients from E&R experiments. The model has overlapping generations, a feature that describes several experimental designs found in the literature. We tested our method under several different demographic and experimental conditions to assess its accuracy and precision, and it performs well in most scenarios. However, some care must be taken when analysing specific allele trajectories, particularly those where drift largely dominates and starting frequencies are low. We compare our method with other available software and report that ours has generally high accuracy even for very difficult trajectories. Furthermore, our approach avoids the computational burden of simulating an empirical null distribution, outperforming available software in terms of computational time and facilitating its use on genome-wide data. We implemented and released our method in a new open-source software package that can be accessed at https://github.com/mrborges23/Bait-ER.


2022 ◽  
Vol 15 (1) ◽  
pp. 45-73
Author(s):  
Andrew Zammit-Mangion ◽  
Michael Bertolacci ◽  
Jenny Fisher ◽  
Ann Stavert ◽  
Matthew Rigby ◽  
...  

Abstract. WOMBAT (the WOllongong Methodology for Bayesian Assimilation of Trace-gases) is a fully Bayesian hierarchical statistical framework for flux inversion of trace gases from flask, in situ, and remotely sensed data. WOMBAT extends the conventional Bayesian synthesis framework through the consideration of a correlated error term, the capacity for online bias correction, and the provision of uncertainty quantification on all unknowns that appear in the Bayesian statistical model. We show, in an observing system simulation experiment (OSSE), that these extensions are crucial when the data are indeed biased and have errors that are spatio-temporally correlated. Using the GEOS-Chem atmospheric transport model, we show that WOMBAT is able to obtain posterior means and variances on non-fossil-fuel CO2 fluxes from Orbiting Carbon Observatory-2 (OCO-2) data that are comparable to those from the Model Intercomparison Project (MIP) reported in Crowell et al. (2019). We also find that WOMBAT's predictions of out-of-sample retrievals obtained from the Total Column Carbon Observing Network (TCCON) are, for the most part, more accurate than those made by the MIP participants.


2021 ◽  
Vol 162 (6) ◽  
pp. 304
Author(s):  
Jacob Golomb ◽  
Graça Rocha ◽  
Tiffany Meshkat ◽  
Michael Bottom ◽  
Dimitri Mawet ◽  
...  

Abstract The work presented here attempts at answering the following question: how do we decide when a given detection is a planet or just residual noise in exoplanet direct imaging data? To this end we implement a metric meant to replace the empirical frequentist-based thresholds for detection. Our method, implemented within a Bayesian framework, introduces an “evidence-based” approach to help decide whether a given detection is a true planet or just noise. We apply this metric jointly with a postprocessing technique and Karhunen–Loeve Image Processing (KLIP), which models and subtracts the stellar PSF from the image. As a proof of concept we implemented a new routine named PlanetEvidence that integrates the nested sampling technique (Multinest) with the KLIP algorithm. This is a first step to recast such a postprocessing method into a fully Bayesian perspective. We test our approach on real direct imaging data, specifically using GPI data of β Pictoris b, and on synthetic data. We find that for the former the method strongly favors the presence of a planet (as expected) and recovers the true parameter posterior distributions. For the latter case our approach allows us to detect (true) dim sources invisible to the naked eye as real planets, rather than background noise, and set a new lower threshold for detection at ∼2.5σ level. Further it allows us to quantify our confidence that a given detection is a real planet and not just residual noise.


2021 ◽  
Vol 40 (1) ◽  
Author(s):  
Owen Paweni Loss Mtambo ◽  
Legesse Kassa Debusho

AbstractThe global prevalence of overweight (including obesity) in children under 5 years of age was 7% in 2012, and it is expected to rise to 11% by the year 2025. The main objective of this study was to fit spatio-temporal quantile interval regression models for childhood overweight (including obesity) in Namibia from 2000 to 2013 using fully Bayesian inference implemented in R-INLA package in R version 3.5.1. All the available Demographic and Health Survey (DHS) datasets for Namibia since 2000 were used in this study. Significant determinants of childhood overweight (including obesity) ranged from socio-demographic factors to child and maternal factors. Child age and preceding birth interval had significant nonlinear effects on childhood overweight (including obesity). Furthermore, we observed significant spatial and temporal effects on childhood overweight (including obesity) in Namibia between 2000 and 2013. To achieve the World Health Organisation (WHO) global nutrition target 2025 in Namibia, the existing scaling-up nutrition programme and childhood malnutrition policy makers in this country may consider interventions based on socio-demographic determinants, and spatio-temporal variations presented in this paper.


Author(s):  
ANDREW T. LITTLE ◽  
KEITH E. SCHNAKENBERG ◽  
IAN R. TURNER

Does motivated reasoning harm democratic accountability? Substantial evidence from political behavior research indicates that voters have “directional motives” beyond accuracy, which is often taken as evidence that they are ill equipped to hold politicians accountable. We develop a model of electoral accountability with voters as motivated reasoners. Directional motives have two effects: (1) divergence—voters with different preferences hold different beliefs, and (2) desensitization—the relationship between incumbent performance and voter beliefs is weakened. While motivated reasoning does harm accountability, this is generally driven by desensitized voters rather than polarized partisans with politically motivated divergent beliefs. We also analyze the relationship between government performance and vote shares, showing that while motivated reasoning always weakens this relationship, we cannot infer that accountability is also harmed. Finally, we show that our model can be mapped to standard models in which voters are fully Bayesian but have different preferences or information.


2021 ◽  
Author(s):  
Simone Puel ◽  
Eldar Khattatov ◽  
Umberto Villa ◽  
Dunyu Liu ◽  
Omar Ghattas ◽  
...  

We introduce a new finite-element (FE) based computational framework to solve forward and inverse elastic deformation problems for earthquake faulting via the adjoint method. Based on two advanced computational libraries, FEniCS and hIPPYlib for the forward and inverse problems, respectively, this framework is flexible, transparent, and easily extensible. We represent a fault discontinuity through a mixed FE elasticity formulation, which approximates the stress with higher order accuracy and exposes the prescribed slip explicitly in the variational form without using conventional split node and decomposition discrete approaches. This also allows the first order optimality condition, i.e., the vanishing of the gradient, to be expressed in continuous form, which leads to consistent discretizations of all field variables, including the slip. We show comparisons with the standard, pure displacement formulation and a model containing an in-plane mode II crack, whose slip is prescribed via the split node technique. We demonstrate the potential of this new computational framework by performing a linear coseismic slip inversion through adjoint-based optimization methods, without requiring computation of elastic Green's functions. Specifically, we consider a penalized least squares formulation, which in a Bayesian setting - under the assumption of Gaussian noise and prior - reflects the negative log of the posterior distribution. The comparison of the inversion results with a standard, linear inverse theory approach based on Okada's solutions shows analogous results. Preliminary uncertainties are estimated via eigenvalue analysis of the Hessian of the penalized least squares objective function. Our implementation is fully open-source and Jupyter notebooks to reproduce our results are provided. The extension to a fully Bayesian framework for detailed uncertainty quantification and non-linear inversions, including for heterogeneous media earthquake problems, will be analyzed in a forthcoming paper.


2021 ◽  
Author(s):  
Connor Donegan

Modeling data collected by areal units, such as counties or census tracts, is a core component of population health research and public resource distribution. Bayesian inference has both practical and philosophical advantages over classical statistical techniques, and advances in Markov chain Monte Carlo (MCMC) are expanding the range of research questions to which fully Bayesian inference may be applied. This code snippet introduces code for fitting spatial conditional autoregressive (CAR) models with the Stan modeling language. Stan is an expressive programming language that uses a dynamic Hamiltonian Monte Carlo (HMC) algorithm to draw samples from user-specified probability models. This paper discusses various CAR model specifications and introduces computationally efficient implementations for Stan users. The paper demonstrates use of the code by modeling United States county mortality data, including censored observations.


2021 ◽  
pp. 014662162110404
Author(s):  
Naidan Tu ◽  
Bo Zhang ◽  
Lawrence Angrave ◽  
Tianjun Sun

Over the past couple of decades, there has been an increasing interest in adopting ideal point models to represent noncognitive constructs, as they have been demonstrated to better measure typical behaviors than traditional dominance models do. The generalized graded unfolding model ( GGUM) has consistently been the most popular ideal point model among researchers and practitioners. However, the GGUM2004 software and the later developed GGUM package in R can only handle unidimensional models despite the fact that many noncognitive constructs are multidimensional in nature. In addition, GGUM2004 and the GGUM package often yield unreasonable estimates of item parameters and standard errors. To address these issues, we developed the new open-source bmggum R package that is capable of estimating both unidimensional and multidimensional GGUM using a fully Bayesian approach, with supporting capabilities of stabilizing parameterization, incorporating person covariates, estimating constrained models, providing fit diagnostics, producing convergence metrics, and effectively handling missing data.


2021 ◽  
Author(s):  
Andrew T. Little

Many experimental and observational studies use the way that subjects respond to information as evidence that partisan bias or directional motives influence (or do not influence) political beliefs. For a natural and tractable formulation belief formation with both accuracy and directional motives, this is not possible. Any subject influenced by directional motives has a "Fully Bayesian Equivalent" with identical beliefs upon observing any signal. As a result, comparing how individuals or groups with different partisanship or priors respond to information has no diagnostic value in detecting motivated reasoning, even in a multivariate or dynamic setting. Conversely, providing a ``Bayesian rationalization'' consistent with a pattern of updating is not meaningful evidence for a lack of directional motives. These results have theoretical implications for the convergence of beliefs among those with directional motives and practical implications for empirical studies that aim to detect directional motives.


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