sbi-dev/sbi

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 you covered.

Language: Python - Size: 109 MB - Last synced at: 3 days ago - Pushed at: 11 days ago - Stars: 687 - Forks: 188

ICB-DCM/pyABC

distributed, likelihood-free inference

Language: Python - Size: 46.4 MB - Last synced at: 3 days ago - Pushed at: 6 months ago - Stars: 214 - Forks: 44

undark-lab/swyft

A system for scientific simulation-based inference at scale.

Language: Jupyter Notebook - Size: 380 MB - Last synced at: 3 days ago - Pushed at: about 1 year ago - Stars: 164 - Forks: 15

probabilists/lampe

Likelihood-free AMortized Posterior Estimation with PyTorch

Language: Python - Size: 4.09 MB - Last synced at: 9 months ago - Pushed at: 9 months ago - Stars: 115 - Forks: 10

smsharma/awesome-neural-sbi

Community-sourced list of papers and resources on neural simulation-based inference.

Size: 104 KB - Last synced at: 9 days ago - Pushed at: 9 days ago - Stars: 114 - Forks: 8

kimmo1019/Roundtrip

Roundtrip: density estimation with deep generative neural networks

Language: Python - Size: 2.27 MB - Last synced at: 10 days ago - Pushed at: about 1 year ago - Stars: 61 - Forks: 14

florent-leclercq/Bayes_InfoTheory

Lectures on Bayesian statistics and information theory

Language: Jupyter Notebook - Size: 31.5 MB - Last synced at: 27 days ago - Pushed at: 27 days ago - Stars: 31 - Forks: 4

msainsburydale/NeuralEstimators.jl

Julia package for neural estimation

Language: Julia - Size: 8.19 MB - Last synced at: 3 days ago - Pushed at: 3 days ago - Stars: 21 - Forks: 2

JoeriHermans/constraining-dark-matter-with-stellar-streams-and-ml

Probing the nature of dark matter by inferring the dark matter particle mass with machine learning and stellar streams.

Language: Jupyter Notebook - Size: 12.8 MB - Last synced at: about 1 month ago - Pushed at: over 2 years ago - Stars: 18 - Forks: 3

montefiore-institute/balanced-nre

Code for the paper "Towards Reliable Simulation-Based Inference with Balanced Neural Ratio Estimation".

Language: Jupyter Notebook - Size: 2.98 MB - Last synced at: 11 months ago - Pushed at: over 2 years ago - Stars: 12 - Forks: 2

JBris/model-calibration-evaluation

Evaluating model calibration methods for sensitivity analysis, uncertainty analysis, optimisation, and Bayesian inference

Language: Python - Size: 4.18 MB - Last synced at: 1 day ago - Pushed at: about 2 months ago - Stars: 11 - Forks: 1

florent-leclercq/pyselfi

Simulator Expansion for Likelihood-Free Inference (SELFI): a python implementation

Language: Jupyter Notebook - Size: 44.7 MB - Last synced at: 23 days ago - Pushed at: over 2 years ago - Stars: 10 - Forks: 1

smsharma/jax-conditional-flows

Normalizing flow models allowing for a conditioning context, implemented using Jax, Flax, and Distrax.

Language: Jupyter Notebook - Size: 1.31 MB - Last synced at: about 1 year ago - Pushed at: about 1 year ago - Stars: 7 - Forks: 3

JuliaApproxInference/LikelihoodfreeInference.jl

Likelihood-Free Inference for Julia.

Language: Julia - Size: 11.2 MB - Last synced at: about 2 years ago - Pushed at: about 3 years ago - Stars: 5 - Forks: 0

GilesStrong/pytorch_inferno

PyTorch implementation of inference aware neural optimisation (de Castro and Dorigo, 2018 https://www.sciencedirect.com/science/article/pii/S0010465519301948)

Language: Jupyter Notebook - Size: 30.8 MB - Last synced at: about 18 hours ago - Pushed at: about 2 years ago - Stars: 4 - Forks: 1

florent-leclercq/correlations_vs_field

Correlation functions versus field-level inference in cosmology: example with log-normal fields

Language: Jupyter Notebook - Size: 130 MB - Last synced at: over 1 year ago - Pushed at: over 2 years ago - Stars: 4 - Forks: 0

pablodecm/paper-inferno

Code and manuscript for the paper "INFERNO: Inference-Aware Neural Optimisation". Automated mirror from CERN GitLab.

Language: Python - Size: 1.51 MB - Last synced at: about 1 year ago - Pushed at: almost 5 years ago - Stars: 4 - Forks: 4

florent-leclercq/lotkavolterra_simulator

Simulator of the Lotka-Volterra prey-predator system with demographic and observational noise and biases

Language: Jupyter Notebook - Size: 1.37 MB - Last synced at: about 13 hours ago - Pushed at: about 1 month ago - Stars: 3 - Forks: 0

JBris/deep-root-gen

A simulation model for the digital reconstruction of 3D root system architectures. Integrated with a simulation-based inference generative deep learning model.

Language: Python - Size: 4.71 MB - Last synced at: about 1 month ago - Pushed at: 6 months ago - Stars: 3 - Forks: 1

francois-rozet/amnre 📦

Arbitrary Marginal Neural Ratio Estimation for Likelihood-free Inference

Language: Python - Size: 3.84 MB - Last synced at: about 2 years ago - Pushed at: almost 3 years ago - Stars: 2 - Forks: 0

ocbe-uio/elfi Fork of elfi-dev/elfi

A Python package for likelihood-free inference (LFI) methods such as Approximate Bayesian Computation (ABC)

Language: Python - Size: 1.27 MB - Last synced at: 8 months ago - Pushed at: about 4 years ago - Stars: 2 - Forks: 0

JBris/likelihood_free_hyperpriors_test

Testing out hyper-priors for likelihood-free methods

Language: Jupyter Notebook - Size: 354 KB - Last synced at: 16 days ago - Pushed at: 16 days ago - Stars: 1 - Forks: 0

jaspervrugt/ABC-PMC

Approximation Bayesian Computation: Population Monte Carlo in MATLAB and Python

Language: Python - Size: 327 KB - Last synced at: 2 months ago - Pushed at: 2 months ago - Stars: 1 - Forks: 0

jmwalchessen/neural_likelihood

Code for "Neural Likelihood Surfaces for Spatial Processes with Computationally Intensive or Intractable Likelihoods" (arxiv:2305.04634)

Language: Jupyter Notebook - Size: 41.1 MB - Last synced at: over 1 year ago - Pushed at: over 1 year ago - Stars: 1 - Forks: 1

JuliaEpi/MathepiaInference.jl

Bayesian inference tools. Including state-of-the-art inference methods: HMC family, ABC family, Data assimilation, and so on. Part of Mathepia.jl

Language: Julia - Size: 96.7 KB - Last synced at: over 1 year ago - Pushed at: almost 3 years ago - Stars: 1 - Forks: 0

nicolossus/pylfi

pyLFI is a Python toolbox using likelihood-free inference (LFI) methods for estimating the posterior distributions of model parameters.

Language: Python - Size: 9.31 MB - Last synced at: 2 months ago - Pushed at: over 3 years ago - Stars: 1 - Forks: 0

ocbe-uio/PriorElicitation

Shiny application for prior elicitation experiments from "Probabilistic elicitation of expert knowledge through assessment of computer simulations"

Language: R - Size: 285 KB - Last synced at: 24 days ago - Pushed at: over 3 years ago - Stars: 1 - Forks: 0

gutmanngroup/minebed Fork of stevenkleinegesse/minebed

Source code for Bayesian Experimental Design for Implicit Models by Mutual Information Neural Estimation, ICML 2020, https://arxiv.org/abs/2002.08129

Size: 2.44 MB - Last synced at: over 1 year ago - Pushed at: about 4 years ago - Stars: 1 - Forks: 0

mjvakili/ccppabc

Approximate Bayesian Computation

Language: Jupyter Notebook - Size: 52.8 MB - Last synced at: about 1 year ago - Pushed at: about 8 years ago - Stars: 1 - Forks: 1

DanielMPMatCom/Detecting-Interactions.-JCE-MatCom

Research on the interactions of a chorus of Eleutherodactylus eileenae. Modeling the biological system using the Ising Model to infer interaction parameters with Gradient Descent.

Language: TeX - Size: 6.99 MB - Last synced at: 7 days ago - Pushed at: 7 days ago - Stars: 0 - Forks: 0

tillahoffmann/summaries2

Language: Python - Size: 171 KB - Last synced at: 11 days ago - Pushed at: 11 days ago - Stars: 0 - Forks: 0

NoemiAM/mist

Tests for model misspecification in simulation-based inference: from local distortions to global model checks. Code repository associated with https://arxiv.org/abs/2412.15100.

Language: Jupyter Notebook - Size: 16.4 MB - Last synced at: 25 days ago - Pushed at: 25 days ago - Stars: 0 - Forks: 0

andrejobuljen/CNN-HI

Cosmology from HI maps using CNNs

Language: Jupyter Notebook - Size: 11.1 MB - Last synced at: 12 months ago - Pushed at: 12 months ago - Stars: 0 - Forks: 0

jmwalchessen/neural_likelihood_interactive_app

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"

Language: Python - Size: 679 KB - Last synced at: over 1 year ago - Pushed at: over 1 year ago - Stars: 0 - Forks: 0

tillahoffmann/summaries

Comparison of summary statistic selection methods with a unifying perspective.

Language: Python - Size: 229 KB - Last synced at: 24 days ago - Pushed at: almost 2 years ago - Stars: 0 - Forks: 0

leonrenn/MINING-GOLD-FROM-GENERATORS

Mining gold from implicit models to improve likelihood-free inference, example for ROLR and RASCAL.

Language: Jupyter Notebook - Size: 446 KB - Last synced at: about 2 years ago - Pushed at: about 2 years ago - Stars: 0 - Forks: 0

tillahoffmann/coaloracle

Repository for simulated genetic data presented by Nunes and Balding (2010).

Language: Makefile - Size: 9.77 KB - Last synced at: 2 months ago - Pushed at: almost 3 years ago - Stars: 0 - Forks: 0

gutmanngroup/idad Fork of desi-ivanova/idad

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

Size: 72.3 KB - Last synced at: over 1 year ago - Pushed at: over 3 years ago - Stars: 0 - Forks: 0

gutmanngroup/GradBED Fork of stevenkleinegesse/GradBED

Code for the paper "Gradient-Based Bayesian Experimental Design for Implicit Models using Mutual Information Lower Bounds" https://arxiv.org/abs/2105.04379

Size: 1.47 MB - Last synced at: over 1 year ago - Pushed at: almost 4 years ago - Stars: 0 - Forks: 0

lucien1011/abc-ml-inference

My framework to perform likelihood-free inference with toy models or real-life simulation

Language: Python - Size: 34.2 KB - Last synced at: about 1 year ago - Pushed at: over 4 years ago - Stars: 0 - Forks: 0

gutmanngroup/bedimplicit Fork of stevenkleinegesse/bedimplicit

Source code for "Efficient Bayesian Experimental Design for Implicit Models", AISTATS 2019, https://arxiv.org/abs/1810.09912

Size: 1.21 MB - Last synced at: over 1 year ago - Pushed at: over 4 years ago - Stars: 0 - Forks: 0