In this work, we address a question that has attracted intense interest in recent years: whether machine learning-assisted algorithms can genuinely outperform classical approaches in challenging ...

Abstract: Bayesian inference provides a methodology for parameter estimation and uncertainty quantification in machine learning and deep learning methods. Variational inference and Markov Chain ...

PyVBMC is a Python implementation of the Variational Bayesian Monte Carlo (VBMC) algorithm for posterior and model inference, previously implemented in MATLAB. VBMC is an approximate inference method ...

particle filtering: bootstrap filter, guided filter, APF. resampling: multinomial, residual, stratified, systematic and SSP. possibility to define state-space models using some (basic) form of ...

Introduction: Due to the limited spatial resolution of SPECT imaging, the partial volume effect results in a substantial reduction in quantitative accuracy. The current clinical standard for partial ...