2019-01-14

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2017-08-02

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2016-11-30
We consider the problem of parametric statistical inference when likelihood computations are prohibitively expensive but sampling from the model is possible. Several so-called likelihood-free methods have been developed to perform inference in the absence of a likelihood function. The popular synthetic likelihood approach infers the parameters by modelling summary statistics of the data by a Gaussian probability distribution. In another popular approach called approximate Bayesian computation, the inference is performed by identifying parameter values for which the summary statistics of the simulated data are close to those of the observed data. Synthetic likelihood is easier to use as no measure of "close-ness" is required but the Gaussianity assumption is often limiting. Moreover, both approaches require judiciously chosen summary statistics. We here present an alternative inference approach that is as easy to use as synthetic likelihood but not as restricted in its assumptions, and that, in a natural way, enables automatic selection of relevant summary statistic from a large set of candidates. The basic idea is to frame the problem of estimating the posterior as a problem of estimating the ratio between the data generating distribution and the marginal distribution. This problem can be solved by logistic regression, and including regularising penalty terms enables automatic selection of the summary statistics relevant to the inference task. We illustrate the general theory on canonical examples and employ it to perform inference for challenging stochastic nonlinear dynamical systems and high-dimensional summary statistics.
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2016-09-07

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2019-02-13

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2018-10-23
Reconstructing the position of an interaction for any dual-phase timeprojection chamber (TPC) with the best precision is key to directly detectingDark Matter. Using the likelihood-free framework, a new algorithm toreconstruct the 2-D (x; y) position and the size of the charge signal (e) of aninteraction is presented. The algorithm uses the charge signal (S2) lightdistribution obtained by simulating events using a waveform generator. To dealwith the computational effort required by the likelihood-free approach, weemploy the Bayesian Optimization for Likelihood-Free Inference (BOLFI)algorithm. Together with BOLFI, prior distributions for the parameters ofinterest (x; y; e) and highly informative discrepancy measures to perform theanalyses are introduced. We evaluate the quality of the proposed algorithm by acomparison against the currently existing alternative methods using alarge-scale simulation study. BOLFI provides a natural probabilisticuncertainty measure for the reconstruction and it improved the accuracy of thereconstruction over the next best algorithm by up to 15% when focusing onevents over a large radii (R > 30 cmcm, the outer 37% of the detector). Inaddition, BOLFI provides the smallest uncertainties among all the testedmethods.
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2018-07-18

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2018-07-16

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2017-06-26

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