Rafael Izbicki | PhD
Rafael Izbicki | PhD
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ABC
Likelihood-Free Frequentist Inference: Confidence Sets with Correct Conditional Coverage
Many areas of science make extensive use of computer simulators that implicitly encode likelihood functions of complex systems. …
N. Dalmasso
,
L. Masserano
,
D. Zhao
,
Rafael Izbicki
,
A. B. Lee
April, 2022
Submitted
PDF
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
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
ABC-CDE: Toward Approximate Bayesian Computation with Complex High-Dimensional Data and Limited Simulations
We show how a nonparametric conditional density estimation (CDE) framework helps address three nontrivial challenges in ABC. (i) how to efficiently estimate the posterior distribution with limited simulations and different types of data, (ii) how to tune and compare the performance of ABC and related methods in estimating the posterior itself, rather than just certain properties of the density, and (iii) how to efficiently choose among a large set of summary statistics based on a CDE surrogate loss.
Rafael Izbicki
,
Taylor Pospisil
,
Ann B. Lee
February, 2019
Journal of Computational and Graphical Statistics
Preprint
PDF
High-Dimensional density ratio estimation with extensions to approximate likelihood computation
The ratio between two probability density functions is an important component of various tasks, including selection bias correction, …
Rafael Izbicki
,
A. B. Lee
,
C. M. Schafer
December, 2014
Journal of Machine Learning Research (AISTATS Track)
PDF
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