• Corpus ID: 88516893

An easy-to-use empirical likelihood ABC method

@article{Chaudhuri2018AnEE,
  title={An easy-to-use empirical likelihood ABC method},
  author={Sanjay Chaudhuri and Subhro Ghosh and David J. Nott and Kim Cuc Pham},
  journal={arXiv: Computation},
  year={2018}
}
Many scientifically well-motivated statistical models in natural, engineering and environmental sciences are specified through a generative process, but in some cases it may not be possible to write down a likelihood for these models analytically. Approximate Bayesian computation (ABC) methods, which allow Bayesian inference in these situations, are typically computationally intensive. Recently, computationally attractive empirical likelihood based ABC methods have been suggested in the… 

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References

SHOWING 1-10 OF 39 REFERENCES

Bayesian Synthetic Likelihood

TLDR
The accuracy and computational efficiency of the Bayesian version of the synthetic likelihood (BSL) approach is explored in comparison to a competitor known as approximate Bayesian computation (ABC) and its sensitivity to its tuning parameters and assumptions.

Bayesian computation via empirical likelihood

TLDR
The Bayesian computation with empirical likelihood algorithm developed in this paper provides an evaluation of its own performance through an associated effective sample size and is illustrated using several examples, including estimation of standard distributions, time series, and population genetics models.

Bayesian indirect inference using a parametric auxiliary model

TLDR
A novel framework called Bayesian indirect likelihood (BIL) is created which encompasses pBII as well as general ABC methods so that the connections between the methods can be established.

Constructing summary statistics for approximate Bayesian computation: semi‐automatic approximate Bayesian computation

TLDR
This work shows how to construct appropriate summary statistics for ABC in a semi‐automatic manner, and shows that optimal summary statistics are the posterior means of the parameters.

A note on approximating ABC‐MCMC using flexible classifiers

TLDR
Approximating the Bayes rule using simulated data from the model and modern flexible classifiers capable of dealing with high‐dimensional feature vectors results in new approximate Bayesian computation procedures that are able to perform well with high-dimensional summary statistics.

An Extended Empirical Saddlepoint Approximation for Intractable Likelihoods

TLDR
A novel, more flexible, density estimator is proposed: the Extended Empirical Saddlepoint approximation, which is able to capture large departures from normality, while being scalable to high dimensions, and this in turn leads to more accurate parameter estimates, relative to the Gaussian alternative.

Approximate Bayesian computational methods

TLDR
In this survey, the various improvements and extensions brought on the original ABC algorithm in recent years are studied.

Approximate Bayesian computation in population genetics.

TLDR
A key advantage of the method is that the nuisance parameters are automatically integrated out in the simulation step, so that the large numbers of nuisance parameters that arise in population genetics problems can be handled without difficulty.

Data cloning: easy maximum likelihood estimation for complex ecological models using Bayesian Markov chain Monte Carlo methods.

TLDR
This work introduces a new statistical computing method, called data cloning, to calculate maximum likelihood estimates and their standard errors for complex ecological models, which is particularly useful for analysing ecological situations in which hierarchical statistical models are appropriate.