Search Results for author:
Found 6 papers, 5 papers with code
BayesFlow: Amortized Bayesian Workflows With Neural Networks
Modern Bayesian inference involves a mixture of computational techniques for estimating, validating, and drawing conclusions from probabilistic models as part of principled workflows for data analysis.
pyPESTO: A modular and scalable tool for parameter estimation for dynamic models
Mechanistic models are important tools to describe and understand biological processes.
A Wall-time Minimizing Parallelization Strategy for Approximate Bayesian Computation
Evaluation of the strategy on different problems and numbers of parallel cores reveals speed-ups of typically 10-20% and up to 50% compared to the best established approach.
pyABC: Efficient and robust easy-to-use approximate Bayesian computation
The Python package pyABC provides a framework for approximate Bayesian computation (ABC), a likelihood-free parameter inference method popular in many research areas.
AMICI: High-Performance Sensitivity Analysis for Large Ordinary Differential Equation Models
Ordinary differential equation models facilitate the understanding of cellular signal transduction and other biological processes.
Uncertainty Quantification
Vocal Bursts Intensity Prediction
Inferring the effect of interventions on COVID-19 transmission networks
Countries around the world implement nonpharmaceutical interventions (NPIs) to mitigate the spread of COVID-19.
