# 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…

## 5 Citations

### Bayesian inference using synthetic likelihood: asymptotics and adjustments

- Computer Science, MathematicsJournal of the American Statistical Association
- 2022

It is shown that Bayesian synthetic likelihood is computationally more efficient than approximate Bayesian computation, and behaves similarly to regression-adjusted approximate Bayesesian computation.

### Empirical Likelihood Under Mis-specification: Degeneracies and Random Critical Points

- Mathematics
- 2019

We investigate empirical likelihood obtained from mis-specified (i.e. biased) estimating equations. We establish that the behaviour of the optimal weights under mis-specification differ markedly from…

### Maximum Likelihood under constraints: Degeneracies and Random Critical Points

- Mathematics
- 2019

We investigate the problem of semi-parametric maximum likelihood under constraints on summary statistics. Such a procedure results in a discrete probability distribution that maximises the likelihood…

### Incorporating Contact Network Uncertainty in Individual Level Models of Infectious Disease using Approximate Bayesian Computation

- Computer ScienceThe international journal of biostatistics
- 2019

This work investigates the fitting of network-based infectious disease models with completely unknown contact networks using approximate Bayesian computation population Monte Carlo (ABC-PMC) methods, and shows that ABC- PMC is able to obtain reasonable approximations of the underlying infectious disease model with huge savings in computation time when compared to a full Bayesian MCMC analysis.

### Comparative phylogeographic inference with genome‐wide data from aggregated population pairs

- BiologyEvolution; international journal of organic evolution
- 2020

It is found that the lampreys and bird population pairs exhibited temporal synchrony in both co‐divergence and collective secondary contact times, yet an idiosyncratic pattern in secondary migration intensities, opening up new possibilities for comparative phylogeography and population genomic studies.

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