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Simulations in Statistical Workflows
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Simulations in Statistical Workflows
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Simulations play important and diverse roles in statistical workflows, for example, in model specification, checking, validation, and even directly in model inference. Over the past decades, the application areas and overall potential of simulations in statistical workflows have expanded significantly, driven by the development of new simulation-based algorithms and exponentially increasing computational resources. In this paper, we examine past and current trends in the field and offer perspectives on how simulations may shape the future of statistical practice.
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Cited by 1 Pith paper
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Overcoming Selection Bias in Statistical Studies With Amortized Bayesian Inference
Embedding selection mechanisms into generative simulators enables amortized Bayesian inference to produce debiased, well-calibrated posteriors without tractable likelihoods.
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