-
# Likelihood-Free Inference Toolbox
Development of a common framework for likelihood inference based on a probabilistic programming language.
Contacts: @EiffL
Participants: @jan-matthis @hpeso…
EiffL updated
5 years ago
-
Hello,
i have maybe a more type of general question. Is it still possible to perform Bayesian inference when the likelihood is basically deterministic. In my problem set up, i have a prior about s…
-
# Neural Likelihood Free Inference – List of papers using Neural Networks for Bayesian Likelihood-Free Inference
List of papers using Neural Networks for Bayesian Likelihood-Free Inference
[https://…
-
The paper "Likelihood-free parameter estimation with neural Bayes estimators" (Sainsbury-Dale, Zammit-Mangion, & Huser, 2023) enables neural amortized *point* estimation, which is generally faster tha…
-
[Greenberg et al. 2019](http://proceedings.mlr.press/v97/greenberg19a/greenberg19a.pdf) uses normalizing flows to perform Bayesian inference on simulators with intractable likelihoods. This seems clos…
-
Presentation slides: https://docs.google.com/presentation/d/1UUC56AFV6-b5c_bdOu9Whto0HwHTJRf2uwYgvPgNsbI/edit?usp=sharing
-
Paper on geometry and metric for comparing distributions (with a focus on GANS): https://arxiv.org/abs/1712.07822
- We could probably find a paper on distribution comparisons more generally if we wan…
-
The inference programming language currently has functions that expose the underlying trace API more or less the way it's implemented:
- `select :: scope -> block -> Action subproblem`
- `detach :: su…
-
# High-dimensional, unbinned deconvolution with OmniFold
One sentence description: Removing detector distortions using likelihood free inference, based on 1911.09107.
Contacts: Ben Nachman
Part…
-
A rough draft of roadmap, feel free to edit:
- [ ] English version
- [ ] Random variables: Multivariable Gaussian, sub-Gaussian
- [ ] Concentration: Hoeffding, Azuma-Hoeffding, Bernstein
- Estim…
TURX updated
7 months ago