
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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Online Statistical Inference for Parameters Estimation with LinearEquality Constraints
Stochastic gradient descent (SGD) and projected stochastic gradient desc...
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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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Stochastic gradient descent for linear least squares problems with partially observed data
We propose a novel stochastic gradient descent method for solving linear...
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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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Data Consistency Approach to Model Validation
In scientific inference problems, the underlying statistical modeling as...
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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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