Search Results for author:
Found 7 papers, 4 papers with code
Cost-aware simulation-based inference
Simulation-based inference (SBI) is the preferred framework for estimating parameters of intractable models in science and engineering.
Bayesian Active Learning in the Presence of Nuisance Parameters
However, the introduction of nuisance parameters can lead to bias in the Bayesian learner's estimate of the target parameters, a phenomenon we refer to as negative interference.
Learning Robust Statistics for Simulation-based Inference under Model Misspecification
Simulation-based inference (SBI) methods such as approximate Bayesian computation (ABC), synthetic likelihood, and neural posterior estimation (NPE) rely on simulating statistics to infer parameters of intractable likelihood models.
Learning Relevant Contextual Variables Within Bayesian Optimization
Contextual Bayesian Optimization (CBO) efficiently optimizes black-box functions with respect to design variables, while simultaneously integrating contextual information regarding the environment, such as experimental conditions.
Optimally-Weighted Estimators of the Maximum Mean Discrepancy for Likelihood-Free Inference
Likelihood-free inference methods typically make use of a distance between simulated and real data.
Approximate Bayesian Computation with Domain Expert in the Loop
Approximate Bayesian computation (ABC) is a popular likelihood-free inference method for models with intractable likelihood functions.
A General Method for Calibrating Stochastic Radio Channel Models with Kernels
We evaluate the performance of the proposed method by fitting two different stochastic channel models, namely the Saleh-Valenzuela model and the propagation graph model, to both simulated and measured data.
