Efficient Bayesian Uncertainty Estimation for nnU-Net

Bayesian effect estimation accounting for adjustment uncertainty.

Model-based estimation of the effect of an exposure on an outcome is generally sensitive to the choice of which confounding factors are included in the model. We propose a new approach, which we call Bayesian adjustment for confounding (BAC), to estimate the effect of an exposure of interest on the outcome, while accounting for the uncertainty in the choice of confounders. Our approach is based...

Efficient Bayesian Phase Estimation.

We introduce a new method called rejection filtering that we use to perform adaptive Bayesian phase estimation. Our approach has several advantages: it is classically efficient, easy to implement, achieves Heisenberg limited scaling, resists depolarizing noise, tracks time-dependent eigenstates, recovers from failures, and can be run on a field programmable gate array. It also outperforms exist...

Quantifying the uncertainty of a belief net response: Bayesian error-bars for belief net inference

A Bayesian belief network models a joint distribution over variables using a DAG to represent variable dependencies and network parameters to represent the conditional probability of each variable given an assignment to its immediate parents. Existing algorithms assume each network parameter is fixed. From a Bayesian perspective, however, these network parameters can be random variables that re...

Bayesian Ambient Noise Inversion for Geoacoustic Uncertainty Estimation: Final report

Bayesian Ambient Noise Inversion for Geoacoustic Uncertainty Estimation

Estimation of seabed geoacoustic parameters in shallow water by remote sensing remains a challenging task due to constraints on hardware, data collection and analysis, and cost of maritime surveys. This work focuses on the application of two techniques that might offer a solution to those constraints: the use of ambient noise to probe the seabed, and Bayesian inversion of these data to estimate...