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abc: an R package for Approximate Bayesian Computation (ABC)
abc package Approximate Bayesian Computation
2011/7/7
Many recent statistical applications involve inference under complex models, where it is computationally prohibitive to calculate likelihoods but possible to simulate data.
Deviance Information Criteria for Model Selection in Approximate Bayesian Computation
Approximate Bayesian computation evolutionary genetics statistical
2011/6/16
Approximate Bayesian computation (ABC) is a class of algorithmic
methods in Bayesian inference using statistical summaries and computer
simulations. ABC has become popular in evolutionary genetics a...
On sequential Monte Carlo，partial rejection control and approximate Bayesian computation
Approximate Bayesian computation Bayesian computation Likelihood free inference Sequential Monte Carlo samplers
2010/4/30
We present a sequential Monte Carlo sampler variant of the partial rejection
control algorithm, and show that this variant can be considered as a sequential
Monte Carlo sampler with a modified mutat...
Nested Sampling for General Bayesian Computation
Bayesian computation evidence marginal likelihood algorithm nest annealing phase change model selection
2009/9/21
Nested sampling estimates directly how the likelihood function relates
to prior mass. The evidence (alternatively the marginal likelihood, marginal den-
sity of the data, or the prior predictive) is...
Bayesian Computation and Model Selection in Population Genetics
Bayesian Computation Model Selection Population Genetics
2010/3/17
Until recently, the use of Bayesian inference in population genetics was lim-
ited to a few cases because for many realistic population genetic models the
likelihood function cannot be calculated an...
Approximate Bayesian computation scheme for parameter inference and model selection in dynamical systems
Approximate Bayesian computation scheme parameter inference model selection dynamical systems
2010/3/17
Approximate Bayesian computation methods can be used to evaluate posterior distributions without having to calculate likelihoods. In this paper we discuss and apply an approximate Bayesian computation...