Detection is truncation: studying source populations with truncated marginal neural ratio estimation. Code repository associated with https://arxiv.org/abs/2211.04291.
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Updated
Aug 28, 2023 - Jupyter Notebook
Detection is truncation: studying source populations with truncated marginal neural ratio estimation. Code repository associated with https://arxiv.org/abs/2211.04291.
A Python package for likelihood-free inference (LFI) methods such as Approximate Bayesian Computation (ABC)
Source code for Bayesian Experimental Design for Implicit Models by Mutual Information Neural Estimation, ICML 2020, https://arxiv.org/abs/2002.08129
Simulator of the Lotka-Volterra prey-predator system with demographic and observational noise and biases
Shiny application for prior elicitation experiments from "Probabilistic elicitation of expert knowledge through assessment of computer simulations"
pyLFI is a Python toolbox using likelihood-free inference (LFI) methods for estimating the posterior distributions of model parameters.
Repository for simulated genetic data presented by Nunes and Balding (2010).
Mining gold from implicit models to improve likelihood-free inference, example for ROLR and RASCAL.
My framework to perform likelihood-free inference with toy models or real-life simulation
Source code for "Efficient Bayesian Experimental Design for Implicit Models", AISTATS 2019, https://arxiv.org/abs/1810.09912
Likelihood-Free Inference for Julia.
Correlation functions versus field-level inference in cosmology: example with log-normal fields
Comparison of summary statistic selection methods with a unifying perspective.
Python code for "Implicit Deep Adaptive Design: Policy-Based Experimental Design without Likelihoods", NeurIPS, 2021, https://proceedings.neurips.cc/paper/2021/hash/d811406316b669ad3d370d78b51b1d2e-Abstract.html
This is an interactive app (run on local computer) to visualize neural likelihood surfaces from the paper "Neural Likelihood Surfaces for Spatial Processes with Computationally Intensive or Intractable Likelihoods"
Cosmology from HI maps using CNNs in PyTorch
Julia package for neural estimation
Bayesian inference tools. Including state-of-the-art inference methods: HMC family, ABC family, Data assimilation, and so on. Part of Mathepia.jl
Code for the paper "Gradient-Based Bayesian Experimental Design for Implicit Models using Mutual Information Lower Bounds" https://arxiv.org/abs/2105.04379
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