[PDF] Top 20 Using Approximate Bayesian Computation to Estimate Tuberculosis Transmission Parameters From Genotype Data

... of approximate Bayesian computa- tion to studies involving complex modeling are im- mense, as evidenced by a growing number of articles using this class of methods in population genetics ...The ... See full document

... the approximate Bayesian computation (ABC) or likelihood-free algorithms for model selection, the main methods and techniques which have been proposed in the literature to deal with model selection ... See full document

... all data points to reduce variance, a localized GKDR is pro- posed by averaging over a small neighborhood around the observation in ...weighted using a distance metric measuring the difference between the ... See full document

... training data was generated from the cubic-quintic model, the algorithm tends to favour the simplest model (the cubic ...of parameters is implicitly ...coming from the quintic term and satisfy ... See full document

... new Approximate Bayesian Computation (ABC) algorithm for Bayesian updating of model parameters is proposed in this paper, which combines the ABC principles with the technique of Subset ... See full document

... extracted from the full data by the summary sta- tistics depends on the parameter value (see Equation ...and parameters on a global scale should be ...proposed using a minimum-entropy ... See full document

... the Bayesian approach of Gronau et ...full data given typical population genetic parameters, the data required for this ABC method are short, independent loci as we assume no intralocus ... See full document

... used approximate Bayesian computation (Marjoram et ...to estimate epidemiological parameters for ...available data, but without the need to evaluate a complex likelihood ... See full document

... Approximate Bayesian computation (ABC) is a family of methods for approximate inference, used when likelihoods are impossible or impractical to evaluate numerically but simulating datasets ... See full document

... the data is also essential to inform the choice of summary ...the data that minimise the loss of information (Barnes et ...the parameters (Fearnhead and Prangle, 2012), or em- ploying a machine ... See full document

... simulate data that were representative of our observed data ...our parameters of ...divergence from the source population ...demographic parameters and thus we chose to disregard this ... See full document

... singular transmission model needs to be more than three times as large as the number of unique ances- ...ongoing transmission model is quite high, averaging thou- sands of migration events over a 4-year ... See full document

... The results of this study strongly support the human- mediated intro- duction of C. carassius into England. But what does this mean for the conservation of C. carassius in England, a country that has one of the few ... See full document

... a Bayesian framework, which intrinsically generates information on uncertainty in parameters (Peterka, ...computational Bayesian (or probabilistic) methods are gaining popularity due to advances in ... See full document

... demographic parameters such as population divergence times or migration parameters, model choice is central to many questions in population ...the data could be more important for datasets containing ... See full document

... The rest of this chapter is organised as follows. In Section 5.1, we take a look at a class of algorithms defined in terms of randomised acceptance ratios that are exact, in the sense that the resulting chain is ... See full document

... The model considered in the paper is a linear birth-death process with mutations (BDM) introduced by Tanaka et al. (2006). The process model is defined as follows: each infected individual, hereafter called host, carries ... See full document

... the data refers to childhood mortality among 7055 Eritrean children born in the period 2001 to ...This data is extracted from the Eritrea Demographic and Health Survey (EDHS) which was conducted in ... See full document

... We now turn to stochastic kinetic models for which the posterior does not take a simple form and exhibits strong correlations between components of θ . Such models are used, for example, in systems biology, where ... See full document

... basin using deterministic (top panel) and stochas- tic (bottom panel) SAC-SMA ...calibration data period and the marginal posterior distribution of two selected SAC-SMA model parameters (right-hand ... See full document