Bayesian learning of parameterised quantum circuits
S Duffield, M Benedetti… - … Learning: Science and …, 2023 - iopscience.iop.org
Currently available quantum computers suffer from constraints including hardware noise
and a limited number of qubits. As such, variational quantum algorithms that utilise a …
and a limited number of qubits. As such, variational quantum algorithms that utilise a …
Thermodynamic Bayesian Inference
A fully Bayesian treatment of complicated predictive models (such as deep neural networks)
would enable rigorous uncertainty quantification and the automation of higher-level tasks …
would enable rigorous uncertainty quantification and the automation of higher-level tasks …
Guided sequential ABC schemes for intractable Bayesian models
U Picchini, M Tamborrino - Bayesian Analysis, 2024 - projecteuclid.org
Sequential algorithms such as sequential importance sampling (SIS) and sequential Monte
Carlo (SMC) have proven fundamental in Bayesian inference for models not admitting a …
Carlo (SMC) have proven fundamental in Bayesian inference for models not admitting a …
Component-wise iterative ensemble Kalman inversion for static Bayesian models with unknown measurement error covariance
Abstract The ensemble Kalman filter (EnKF) is a Monte Carlo approximation of the Kalman
filter for high dimensional linear Gaussian state space models. EnKF methods have also …
filter for high dimensional linear Gaussian state space models. EnKF methods have also …
Single-ensemble multilevel Monte Carlo for discrete interacting-particle methods
A Bouillon, T Ingelaere, G Samaey - arXiv preprint arXiv:2405.10146, 2024 - arxiv.org
To solve problems in domains such as filtering, optimization, and posterior sampling,
interacting-particle methods have recently received much attention. These parallelizable …
interacting-particle methods have recently received much attention. These parallelizable …
Ensemble Kalman inversion approximate Bayesian computation
RG Everitt - arXiv preprint arXiv:2407.18721, 2024 - arxiv.org
Approximate Bayesian computation (ABC) is the most popular approach to inferring
parameters in the case where the data model is specified in the form of a simulator. It is not …
parameters in the case where the data model is specified in the form of a simulator. It is not …
Sequential Kalman tuning of the t-preconditioned Crank-Nicolson algorithm: efficient, adaptive and gradient-free inference for Bayesian inverse problems
Abstract Ensemble Kalman Inversion (EKI) has been proposed as an efficient method for the
approximate solution of Bayesian inverse problems with expensive forward models …
approximate solution of Bayesian inverse problems with expensive forward models …
Efficient Bayesian Physics Informed Neural Networks for inverse problems via Ensemble Kalman Inversion
A Pensoneault, X Zhu - Journal of Computational Physics, 2024 - Elsevier
Abstract Bayesian Physics Informed Neural Networks (B-PINNs) have gained significant
attention for inferring physical parameters and learning the forward solutions for problems …
attention for inferring physical parameters and learning the forward solutions for problems …
Quasi-Newton Sequential Monte Carlo
S Duffield, SS Singh - arXiv preprint arXiv:2211.12580, 2022 - arxiv.org
Sequential Monte Carlo samplers represent a compelling approach to posterior inference in
Bayesian models, due to being parallelisable and providing an unbiased estimate of the …
Bayesian models, due to being parallelisable and providing an unbiased estimate of the …
Optimal Power Management of Battery Energy Storage Systems via Ensemble Kalman Inversion
Optimal power management of battery energy storage systems (BESS) is crucial for their
safe and efficient operation. Numerical optimization techniques are frequently utilized to …
safe and efficient operation. Numerical optimization techniques are frequently utilized to …