Efficient Acquisition Rules for Model-Based Approximate Bayesian Computation

@article{Jarvenpaa2019EfficientAR,
  title={Efficient Acquisition Rules for Model-Based Approximate Bayesian Computation},
  author={Marko Jarvenpaa and Michael U. Gutmann and Arijus Pleska and Aki Vehtari and Pekka Marttinen},
  journal={Bayesian Analysis},
  year={2019}
}
Approximate Bayesian computation (ABC) is a method for Bayesian inference when the likelihood is unavailable but simulating from the model is possible. However, many ABC algorithms require a large number of simulations, which can be costly. To reduce the computational cost, Bayesian optimisation (BO) and surrogate models such as Gaussian processes have been proposed. Bayesian optimisation enables one to intelligently decide where to evaluate the model next but common BO strategies are not… 
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References

SHOWING 1-10 OF 67 REFERENCES
A tutorial on approximate Bayesian computation
Approximate Bayesian computation via regression density estimation
TLDR
The contribution of the present paper is to consider regression density estimation techniques to approximate the likelihood in the ABC setting, which builds on recently developed marginal adaptation density estimators by extending them for conditional density estimation.
Approximate Bayesian computation (ABC) gives exact results under the assumption of model error
  • R. Wilkinson
  • Computer Science
    Statistical applications in genetics and molecular biology
  • 2013
TLDR
Under the assumption of the existence of a uniform additive model error term, ABC algorithms give exact results when sufficient summaries are used, which allows the approximation made in many previous application papers to be understood, and should guide the choice of metric and tolerance in future work.
Fast ε-free Inference of Simulation Models with Bayesian Conditional Density Estimation
TLDR
This work proposes a new approach to likelihood-free inference based on Bayesian conditional density estimation, which requires fewer model simulations than Monte Carlo ABC methods need to produce a single sample from an approximate posterior.
Approximate Bayesian computation scheme for parameter inference and model selection in dynamical systems
TLDR
This paper discusses and applies an ABC method based on sequential Monte Carlo (SMC) to estimate parameters of dynamical models and develops ABC SMC as a tool for model selection; given a range of different mathematical descriptions, it is able to choose the best model using the standard Bayesian model selection apparatus.
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.
Accelerating ABC methods using Gaussian processes
TLDR
Gaussian process (GP) accelerated ABC is introduced, which it is shown can significantly reduce the number of simulations required and enable more accurate inference in some models.
GPS-ABC: Gaussian Process Surrogate Approximate Bayesian Computation
TLDR
This work develops two new ABC sampling algorithms that significantly reduce the number of simulations necessary for posterior inference and stores the information obtained from every simulation in a Gaussian process which acts as a surrogate function for the simulated statistics.
Gaussian Processes to Speed up Hybrid Monte Carlo for Expensive Bayesian Integrals
TLDR
This work proposes to use a Gaussian Process model of the (log of the) posterior for most of the computations required by HMC, allowing Bayesian treatment of models with posteriors that are computationally demanding, such as models involving computer simulation.
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.
...
1
2
3
4
5
...