Lectures on Bayesian statistics and information theory
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Updated
Sep 16, 2021 - Jupyter Notebook
Lectures on Bayesian statistics and information theory
Pure julia implementation of Multiple Affine Invariant Sampling for efficient Approximate Bayesian Computation
pyABC: distributed, likelihood-free inference
Bayesian optimisation for fast approximate inference in state-space models with intractable likelihoods
Simulation-based inference in JAX
A toolbox for C++ devs wanting to build geospatial population genetics simulators !
Simulator Expansion for Likelihood-Free Inference (SELFI): a python implementation
ABC random forests for model choice and parameter estimation, pure C++ implementation
Approximate Bayesian Computation (ABC) with differential evolution (de) moves and model evidence (Z) estimates.
Adding Noise Noise Canceling Image resizing Resolution Study Filtering processes -Midic filter -Mean filter -Laplasian filter Photo Sharpening
Evaluating model calibration methods for sensitivity analysis, uncertainty analysis, optimisation, and Bayesian inference
Likelihood-Free Inference for Julia.
User interface to DIYABC/AbcRanger
Trabajo de Fin de Grado de Física 2022
Correlation functions versus field-level inference in cosmology: example with log-normal fields
Figuring out how Approximate Bayesian Computation works and how it can be applied to geological modeling.
Simulator of the Lotka-Volterra prey-predator system with demographic and observational noise and biases
A Python package for likelihood-free inference (LFI) methods such as Approximate Bayesian Computation (ABC)
GPU and TPU implementation of parallelized ABC inference for a stochastic epidemiology model for COVID-19
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