2 citations · 2 across the 2 of their papers we have counts for
4 papers
BioSimulators: a central registry of simulation engines and services for recommending specific tools
Bilal Shaikh, Lucian P. Smith, Dan Vasilescu +68
Computational models have great potential to accelerate bioscience, bioengineering, and medicine. However, it remains challenging to reproduce and reuse simulations, in part, becau…
Robust and integrative Bayesian neural networks for likelihood-free parameter inference
Fredrik Wrede, Robin Eriksson, Richard Jiang +4
State-of-the-art neural network-based methods for learning summary statistics have delivered promising results for simulation-based likelihood-free parameter inference. Existing ap…
Implicit Hamiltonian Monte Carlo for Sampling Multiscale Distributions
Arya A. Pourzanjani, Linda R. Petzold
Hamiltonian Monte Carlo (HMC) has been widely adopted in the statistics community because of its ability to sample high-dimensional distributions much more efficiently than other M…
Selecting the Metric in Hamiltonian Monte Carlo
Ben Bales, Arya Pourzanjani, Aki Vehtari +1
We present a selection criterion for the Euclidean metric adapted during warmup in a Hamiltonian Monte Carlo sampler that makes it possible for a sampler to automatically pick the…