
Classification and Bayesian Optimization for LikelihoodFree Inference
Some statistical models are specified via a data generating process for ...
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ELFI: Engine for Likelihood Free Inference
The Engine for LikelihoodFree Inference (ELFI) is a Python software lib...
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Machine Learning Accelerated LikelihoodFree Event Reconstruction in Dark Matter Direct Detection
Reconstructing the position of an interaction for any dualphase time pr...
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Approximating Likelihood Ratios with Calibrated Discriminative Classifiers
In many fields of science, generalized likelihood ratio tests are establ...
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Data Consistency Approach to Model Validation
In scientific inference problems, the underlying statistical modeling as...
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Likelihoodfree inference via classification
Increasingly complex generative models are being used across disciplines...
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INFERNO: InferenceAware Neural Optimisation
Complex computer simulations are commonly required for accurate data mod...
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Bayesian Optimization for LikelihoodFree Inference of SimulatorBased Statistical Models
Our paper deals with inferring simulatorbased statistical models given some observed data. A simulatorbased model is a parametrized mechanism which specifies how data are generated. It is thus also referred to as generative model. We assume that only a finite number of parameters are of interest and allow the generative process to be very general; it may be a noisy nonlinear dynamical system with an unrestricted number of hidden variables. This weak assumption is useful for devising realistic models but it renders statistical inference very difficult. The main challenge is the intractability of the likelihood function. Several likelihoodfree inference methods have been proposed which share the basic idea of identifying the parameters by finding values for which the discrepancy between simulated and observed data is small. A major obstacle to using these methods is their computational cost. The cost is largely due to the need to repeatedly simulate data sets and the lack of knowledge about how the parameters affect the discrepancy. We propose a strategy which combines probabilistic modeling of the discrepancy with optimization to facilitate likelihoodfree inference. The strategy is implemented using Bayesian optimization and is shown to accelerate the inference through a reduction in the number of required simulations by several orders of magnitude.
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