
Statistical Inference for Model Parameters in Stochastic Gradient Descent via Batch Means
Statistical inference of true model parameters based on stochastic gradi...
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Likelihoodfree inference via classification
Increasingly complex generative models are being used across disciplines...
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Dynamic Likelihoodfree Inference via Ratio Estimation (DIRE)
Parametric statistical models that are implicitly defined in terms of a ...
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Bayesian Optimization for LikelihoodFree Inference of SimulatorBased Statistical Models
Our paper deals with inferring simulatorbased statistical models given ...
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BayesFlow: Learning complex stochastic models with invertible neural networks
Estimating the parameters of mathematical models is a common problem in ...
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Statistical Inference for the Population Landscape via Moment Adjusted Stochastic Gradients
Modern statistical inference tasks often require iterative optimization ...
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Inference of Network Summary Statistics Through Network Denoising
Consider observing an undirected network that is `noisy' in the sense th...
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INFERNO: InferenceAware Neural Optimisation
Complex computer simulations are commonly required for accurate data modelling in many scientific disciplines, making statistical inference challenging due to the intractability of the likelihood evaluation for the observed data. Furthermore, sometimes one is interested on inference drawn over a subset of the generative model parameters while taking into account model uncertainty or misspecification on the remaining nuisance parameters. In this work, we show how nonlinear summary statistics can be constructed by minimising inferencemotivated losses via stochastic gradient descent.
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