Rafael Izbicki | PhD
Rafael Izbicki | PhD
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Approximate Likelihood
Diagnostics for Conditional Density Models and Bayesian Inference Algorithms
D. Zhao
,
N. Dalmasso
,
Rafael Izbicki
,
A. B. Lee
September, 2021
Proceedings of Machine Learning Research (UAI Track)
paper
PDF
Confidence Sets and Hypothesis Testing in a Likelihood-Free Inference Setting
Parameter estimation, statistical tests and confidence sets are the cornerstones of classical statistics that allow scientists to make inferences about the underlying process that generated the observed data. A key question is whether one can still construct hypothesis tests and confidence sets with proper coverage and high power in a so-called likelihood-free inference (LFI) setting; that is, a setting where the likelihood is not explicitly known but one can forward-simulate observable data according to a stochastic model. We present ACORE, a frequentist approach to LFI that first formulates the classical likelihood ratio test (LRT) as a parametrized classification problem, and then uses the equivalence of tests and confidence sets to build confidence regions for parameters of interest. We also present a goodness-of-fit procedure for checking whether the constructed tests and confidence regions are valid.
Niccolò Dalmasso
,
Rafael Izbicki
,
Ann B. Lee
February, 2020
Proceedings of Machine Learning Research (ICML Track)
Preprint
PDF
Validation of Approximate Likelihood and Emulator Models for Computationally Intensive Simulations
Complex phenomena in engineering and the sciences are often modeled with computationally intensive feed-forward simulations for which a …
N. Dalmasso
,
A. B. Lee
,
Rafael Izbicki
,
T. Pospisil
,
I. Kim
,
C. Lin
January, 2020
Proceedings of Machine Learning Research (AISTATS Track)
Preprint
PDF
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