[PDF] Top 20 A Novel Approach for Choosing Summary Statistics in Approximate Bayesian Computation
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A Novel Approach for Choosing Summary Statistics in Approximate Bayesian Computation
... choosing summary statistics locally and compare the local variants of the various methods to their global ...for choosing summary statistics in ...than approximate ... See full document
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Amount of Information Needed for Model Choice in Approximate Bayesian Computation
... of statistics, where the choice of tolerance is very ...0:005), choosing a tolerance of ...the summary statistics capture more features of the site frequency ...of summary ... See full document
13
Approximate Bayesian computation by subset simulation
... of summary statistics η(·) that permits a comparison of the closeness of x and y in a weak ...this approach, the posterior p(θ, x|y) in Equation 2 is approximated by p (θ, x|y), which assigns higher ... See full document
20
Approximate Bayesian Computation in Population Genetics
... feasible approach to statistical inference interest, φ, is simulated from its prior ...comparing summary statistics with their ally, the next step would be to accept φ⬘ with probability null ... See full document
12
Approximate Bayesian computation for infectious disease modelling
... appropriate summary statistic may be to match the time of the two peaks, or the ratio of the size of one peak to ...include summary statistics which is a function of model outputs only, for example, ... See full document
12
A tutorial introduction to Bayesian inference for stochastic epidemic models using Approximate Bayesian Computation
... and summary statistics of the data (function ...below. Summary statistics. An obvious choice for summary statistics would be to calculate the number of removals per day and ... See full document
27
Bridging the gap between GLUE and formal statistical approaches: approximate Bayesian computation
... that approximate Bayesian computation (ABC) bridges the gap between formal and informal statis- tical model–data fitting ...different summary statistics that measure the distance of ... See full document
20
Auxiliary likelihood based approximate Bayesian computation in state space models
... likelihood approach to ABC, including sufficient conditions for Bayesian consistency to hold, in this particular ...likelihood approach in the non-linear state space setting, using the three classes ... See full document
44
A rare event approach to high dimensional approximate Bayesian computation
... 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
16
Automatic kernel selection for Gaussian processes regression with approximate Bayesian computation and sequential Monte Carlo
... a novel combination of two Bayesian tools, Gaussian Processes (GPs), and the use of the Approximate Bayesian Computation (ABC) algorithm for kernel selection and parameter estimation ... See full document
13
Examining Phylogenetic Relationships Among Gibbon Genera Using Whole Genome Sequence Data Using an Approximate Bayesian Computation Approach
... a summary statistic may prove useful in this ...of summary statistics needed to capture the phylogenetic structure ...while choosing a more efficient set of summary statis- tics ...few ... See full document
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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
10
An overview on Approximate Bayesian computation*
... Fearnhead and Prangle (2012) have adopted a different approach in order to construct a vector of summary statistics almost from scratch. When the aim of ABC is estimating θ, their theoretical results ... See full document
9
Bayesian synthetic likelihood
... parametric Bayesian indirect likelihood that uses the likelihood of an alter- native parametric auxiliary model, have been explored throughout the literature as a viable alternative when the model of interest is ... See full document
31
Inferences about the transmission of Schmallenberg virus within and between farms
... In the summer of 2011 Schmallenberg virus (SBV), a Culicoides-borne orthobunyavirus, emerged in Germany and The Netherlands and subsequently spread across much of Europe. To draw inferences about the transmission of SBV ... See full document
11
Model selection and parameter estimation in structural dynamics using approximate Bayesian computation
... The identification of the Bouc-Wen (BW) model has been widely investigated in the literature by various different meth- ods [33]. The highly nonlinear nature of the BW model, along with a large number of model ... See full document
20
Modeling the role of social structures in population genetics : a thesis presented in partial fulfilment of the requirements for the degree of Doctor of Philosophy in Statistics at Massey University, Manawatu, New Zealand
... a novel simulation framework and genome-wide data to explore the effects of Asymmetric Prescriptive Alliance, an elaborate set of marriage rules that has been a focus of research for many ... See full document
139
Stability and examples of some approximate MCMC algorithms
... a novel method, described in Algorithm ...new approach, including sub-sampling for large data ...the approximate chain and the convergence of the approximate stationary ... See full document
148
On the asymptotic efficiency of approximate Bayesian computation estimators
... the summary statistics obey a central limit theorem for large ...of approximate Baysian computation, where by efficiency we mean that an estimator obtained from running Algorithm 1 has the ... See full document
15
Computational system identification of continuous-time nonlinear systems using approximate Bayesian computation
... To solve the structure detection problem, the inspiration for our basic approach comes from Kukreja et al. (2004), where structure detection for a discrete-time nonlinear model was solved by one-step-ahead ... See full document
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