46 citations · 61 across the 9 of their papers we have counts for
9 papers
Variational inference of effective range parameters for He-He scattering
Andrius Burnelis, Vojta Kejzlar, Daniel R. Phillips
We use two different methods, Monte Carlo sampling and variational inference (VI), to perform a Bayesian calibration of the effective-range parameters in He-He elastic…
Model orthogonalization and Bayesian forecast mixing via Principal Component Analysis
Pablo Giuliani, Kyle Godbey, Vojtech Kejzlar +1
One can improve predictability in the unknown domain by combining forecasts of imperfect complex computational models using a Bayesian statistical machine learning framework. In ma…
Local Bayesian Dirichlet mixing of imperfect models
Vojtech Kejzlar, Léo Neufcourt, Witold Nazarewicz
To improve the predictability of complex computational models in the experimentally-unknown domains, we propose a Bayesian statistical machine learning framework utilizing the Diri…
Introducing Variational Inference in Statistics and Data Science Curriculum
Vojtech Kejzlar, Jingchen Hu
Probabilistic models such as logistic regression, Bayesian classification, neural networks, and models for natural language processing, are increasingly more present in both underg…
Black Box Variational Bayesian Model Averaging
Vojtech Kejzlar, Shrijita Bhattacharya, Mookyong Son +1
For many decades now, Bayesian Model Averaging (BMA) has been a popular framework to systematically account for model uncertainty that arises in situations when multiple competing…
A Fast and Calibrated Computer Model Emulator: An Empirical Bayes Approach
Vojtech Kejzlar, Mookyong Son, Shrijita Bhattacharya +1
Mathematical models implemented on a computer have become the driving force behind the acceleration of the cycle of scientific processes. This is because computer models are typica…
Variational Inference with Vine Copulas: An efficient Approach for Bayesian Computer Model Calibration
Vojtech Kejzlar, Tapabrata Maiti
With the advancements of computer architectures, the use of computational models proliferates to solve complex problems in many scientific applications such as nuclear physics and…
Statistical aspects of nuclear mass models
Vojtech Kejzlar, Léo Neufcourt, Witold Nazarewicz +1
We study the information content of nuclear masses from the perspective of global models of nuclear binding energies. To this end, we employ a number of statistical methods and dia…
Bayesian averaging of computer models with domain discrepancies: a nuclear physics perspective
Vojtech Kejzlar, Léo Neufcourt, Taps Maiti +1
This article studies Bayesian model averaging (BMA) in the context of competing expensive computer models in a typical nuclear physics setup. While it is well known that BMA accoun…