[PDF] Top 20 An overview on Approximate Bayesian computation*
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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 ... See full document
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A tutorial introduction to Bayesian inference for stochastic epidemic models using Approximate Bayesian Computation
... and overview of some of the recent work concerned with Approximate Bayesian Computation methods for performing (approximate) Bayesian inference for stochastic epidemic models ... See full document
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Variance bounding and geometric ergodicity of Markov chain Monte Carlo kernels for approximate Bayesian computation
... Approximate Bayesian computation has emerged as a standard computational tool when deal- ing with intractable likelihood functions in Bayesian ... See full document
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Automatic kernel selection for Gaussian processes regression with approximate Bayesian computation and sequential Monte Carlo
... (1995), Kullback (1997), Akaike (1998), Doucet et al. (2000, 2001), Au and Beck (2001), Nabney (2002), Lawrence (2003), Marjoram et al. (2003), Ching et al. (2006), Rasmussen and Williams (2006), Skilling (2006), Gretton ... See full document
13
Bridging the gap between GLUE and formal statistical approaches: approximate Bayesian computation
... as approximate Bayesian computation (ABC) (Marjoram et ...certain approximate Bayesian pro- cedure even when the "generalized likelihood" is not a true ... See full document
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Examining Phylogenetic Relationships Among Gibbon Genera Using Whole Genome Sequence Data Using an Approximate Bayesian Computation Approach
... Analogous to the Bayesian approach of Gronau et al. (2011), which uses an analytical derivation to determine the likelihood of the full data given typical population genetic parameters, the data required for this ... See full document
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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 ... See full document
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Inferences about the transmission of Schmallenberg virus within and between farms
... In the present study we have used approximate Bayesian computation (Marjoram et al., 2003; Toni et al., 2009; Sunnaker et al., 2013) to estimate epidemiological parameters for SBV. This allowed us to ... See full document
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Auxiliary likelihood based approximate Bayesian computation in state space models
... to approximate the features of the true data generating ...a Bayesian framework, the principle underlying the frequentist methods of indirect inference (Gouri´ eroux et ...the Bayesian case is ... See full document
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Using Approximate Bayesian Computation to Estimate Tuberculosis Transmission Parameters From Genotype Data
... an approximate Bayesian computational method in combination with a stochastic model of tuberculosis transmission and mutation of a molecular marker to estimate the net transmission rate, the doubling time, ... See full document
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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 ... See full document
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Model selection and parameter estimation of dynamical systems using a novel variant of approximate Bayesian computation
... the Bayesian approach has been successfully applied in different domains (dynamics, genetics, biology, ecology, ...the Bayesian method for parameter estimation and model selection, the reader is referred to ... See full document
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Comparative Evaluation of a New Effective Population Size Estimator Based on Approximate Bayesian Computation
... Simulations of a Wright-Fisher population with known Ne show that the SummStat estimator is useful across a realistic range of individuals and loci sampled, generations between samples, [r] ... See full document
12
Computational system identification of continuous-time nonlinear systems using approximate Bayesian computation
... In this paper we derive a system identification framework for continuous-time nonlinear systems, for the first time using a simulation-focused computational Bayesian approach. Simulation approaches to non- linear ... See full document
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Approximate Bayesian computation (ABC) gives exact results under the assumption of model error
... Finally, once we have specified a distribution for ε , we may find the acceptance rate is too small to be practicable and that it is necessary to compromise (as in Example 2 above). A pragmatic way to increase the ... See full document
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Asymptotic properties of approximate Bayesian computation
... which approximate Bayesian computation estimates the exact marginal posterior densities when choosing quantiles smaller than α 2 ; whilst in the case of σ, the worst performing estimate is that ... See full document
32
On the asymptotic efficiency of approximate Bayesian computation estimators
... sample. Approximate Bayesian computation is a likelihood-free method for implementing Bayesian inference in such ...using approximate Bayesian computation in a large-data ... See full document
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Convergence of regression adjusted approximate Bayesian computation
... By Condition 2ii, Condition 6 and following the arguments in the proof of Lemma 3 of Li & αδ Fearnhead 2015, the right hand side of 4 is Op e−an,ε cδ , which is sufficient for πBδc {θ − [r] ... See full document
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Convergence of regression adjusted approximate Bayesian computation
... of approximate Bayesian ...proximate Bayesian computation as the amount of data, n, ...in approximate Bayesian computation between choices of the summary statistics and ... See full document
18
Approximate Bayesian Computation in Population Genetics
... for approximate Bayesian statistical inference on the basis of summary ...of Bayesian statistical inference with the computational efficiency of methods based on summary ... See full document
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