approximate Bayesian computation analysis
Amount of Information Needed for Model Choice in Approximate Bayesian Computation
... ABC analysis, including the choice of summary statistics, are important in determining the power to reject a null demographic model in favor of a more complex ...
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Examining Phylogenetic Relationships Among Gibbon Genera Using Whole Genome Sequence Data Using an Approximate Bayesian Computation Approach
... the Bayesian approach of Gronau et ...estimates. Analysis of pseudo-observed data generated by simulations demonstrated that we were able to detect the correct topology from randomly drawn datasets using ...
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Genetic evidence challenges the native status of a threatened freshwater fish (Carassius carassius ) in England
... Such phylogeographic questions are difficult to test. Past ap- proaches have included the use of simple molecular clock calibrations, whereby the amount of molecular diversity that has arisen between two lineages is ...
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Automatic kernel selection for Gaussian processes regression with approximate Bayesian computation and sequential Monte Carlo
... Regression analysis or classification using Bayesian formulation and specifically Gaussian Processes (GPs) or relevance vector machines (RVMs) is becoming very popular and attractive due to incorporation of ...
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Modelling the impact of larviciding on the population dynamics and biting rates of Simulium damnosum (s l ): implications for vector control as a complementary strategy for onchocerciasis elimination in Africa
... proximate Bayesian computation for parameter estimation) to the log likelihood of the observed data were considered as samples from the approximate posterior distribution ...this analysis also ...
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Convergence of regression adjusted approximate Bayesian computation
... Modern statistical applications increasingly require the fitting of complex statistical models. Often these models are intractable in the sense that it is impossible to evaluate the likelihood function. This excludes ...
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Analysis and computation for Bayesian inverse problems
... particular, Bayesian approaches to inverse problems is provided in the text [75], with a strong focus on cases where Lebesgue densities ...to approximate integrals via Monte Carlo approxi- ...
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A tutorial introduction to Bayesian inference for stochastic epidemic models using Approximate Bayesian Computation
... only Bayesian statistics, but have also been developded for fitting stochastic epidemic models to partially observed outbreak data (O’Neill and Roberts, 1999; Gibson and Renshaw, ...the analysis of ...
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Using Approximate Bayesian Computation to Estimate Tuberculosis Transmission Parameters From Genotype Data
... Tolerance level: We investigated the effect of varying the tolerance parameter e . This parameter affects both the computational efficiency and the accuracy of the inference. Unfortunately, with the Markov chain imple- ...
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Approximate Bayesian computation for infectious disease modelling
... Our analysis based on both the ABC-SMC and ABC-SMC NN indicates that having n > 1 increases computation burden and decreases the ESS, which cannot be out-weighted by im- provement in the overall ...
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Approximate Bayesian Computation for Copula Estimation
... Copula models are nowadays widely used in multivariate data analysis. Major ar- eas of application include econometrics (Huynh et al., 2015), geophysics (Scholzel and Friederichs, 2008), quantum mechanics (Resconi ...
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A rare event approach to high dimensional approximate Bayesian computation
... The most popular approach to deal with the curse of dimensionality in ABC is dimension reduction. Here, high- dimensional datasets are mapped to lower dimensional vectors of features, often referred to as summary ...
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Asymptotic properties of approximate Bayesian computation
... Approximate Bayesian computation allows for statistical analysis in models with intractable likeli- hoods. In this paper we consider the asymptotic behaviour of the posterior distribution ...
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Simulation-based estimation of mean and standard deviation for meta-analysis via Approximate Bayesian Computation (ABC)
... We propose a more flexible approach than existing methods to estimate the mean and standard deviation for meta-analysis when only descriptive statistics are available. Our ABC method shows comparable per- formance ...
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Approximate Bayesian computation by subset simulation
... fails in this case (essentially all candidate samples from the proposal PDF are rejected-see the analysis in [1]). In MMA, a univariate proposal PDF is chosen for each component of the parameter vector and each ...
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The rate of convergence for approximate Bayesian computation
... Despite the popularity of ABC methods, theoretical analysis is still in its infancy. The aim of this article is to provide a foundation for such analysis by providing rigorous results about the convergence ...
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Approximate Bayesian Computation in Population Genetics
... the analysis (here and with differing at a particular locus, averaged across loci); and the growth model discussed below) using the median (3) the number of distinct haplotypes in the ...
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An overview on Approximate Bayesian computation*
... (1997), approximate Bayesian computation (ABC) methods have been widely used with intractable ...the computation are given in Section ...
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Approximate Bayesian computation in large-scale structure: constraining the galaxy-halo connection
... Accurate estimation of the covariance matrix in LSS, however, faces a number of challenges. It is both labour and computation- ally expensive and dependent on the accuracy of simulated mock catalogues, known to be ...
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Model selection and parameter estimation in structural dynamics using approximate Bayesian computation
... Furthermore, ABC offers the possibility to manage larger datasets and a higher number of competing models with differ- ent dimensionalities, circumventing the limitation of RJ-MCMC. Besides the major advantages mentioned ...
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