Stars
McMC transdimensional Bayesian inversion of surface wave dispersion and receiver functions
code for the paper "Stein Variational Gradient Descent (SVGD): A General Purpose Bayesian Inference Algorithm"
Numba-accelerated computation of surface wave dispersion
Joint inversion of Receiver Function and Surface Wave Disperion by Hamiltonian Monte Carlo Method
A Collection of Variational Autoencoders (VAE) in PyTorch.
Python wrappers for the CPS and Rftn libraries for layered models in seismology
Pytorch implementation of Universal Boosting Variational Inference
From-scratch diffusion model implemented in PyTorch.
Python wrapper for modelling surface wave dispersion curves from surf96 - Computer Programs in Seismology, R. Hermann
Samplers from the paper "Stochastic Gradient MCMC with Repulsive Forces"
Edinburgh PhD thesis template to use in Overleaf
We got a stew going!
code supplement for variational boosting (https://arxiv.org/abs/1611.06585)
Learning in infinite dimension with neural operators.
An interior-point method written in python for solving constrained and unconstrained nonlinear optimization problems.
A trust-region interior-point method for general nonlinear programing problems (GSoC 2017).
A Julia framework for invertible neural networks
Density Estimation Likelihood-Free Inference with neural density estimators and adaptive acquisition of simulations
Code for paper "Fast ε-free Inference of Simulation Models with Bayesian Conditional Density Estimation"
Density estimation likelihood-free inference. No longer actively developed see https://github.com/mackelab/sbi instead
Conditional density estimation with neural networks
sbi is a Python package for simulation-based inference, designed to meet the needs of both researchers and practitioners. Whether you need fine-grained control or an easy-to-use interface, sbi has …