vacquaviva / Flatiron_MCMC
This is the material (slides + notebook) used to give the MCMC class in Hogg's Computational Data Analytics class at Flatiron CCA on 3/29.
☆11Updated 5 years ago
Alternatives and similar repositories for Flatiron_MCMC:
Users that are interested in Flatiron_MCMC are comparing it to the libraries listed below
- Materials for the Mini Summer School in Machine Learning x Astro taught at CCA in Summer '19☆17Updated 5 years ago
- An experiment: emcee implemented in JAX☆25Updated 2 years ago
- Course materials for Fundamentals of Data Science☆37Updated 4 years ago
- Neural network nested and MCMC sampling☆22Updated 3 years ago
- Spring 2021 course materials for ASTR 8070: Astrostatistics☆29Updated 3 years ago
- ASTR 598: Astro-statistics and Machine Learning☆40Updated 7 years ago
- ☆22Updated 5 years ago
- Combines Bayesian analyses from many datasets.☆22Updated 10 months ago
- Lectures on Bayesian statistics and information theory☆31Updated 3 years ago
- chapters of a book Hogg will never write☆90Updated 2 months ago
- Repository for PHYS T480/580 (Big Data Physics: Methods of Machine Learning) at Drexel University, Fall 2018☆10Updated 6 years ago
- A pipeline for versatile strong lens sample simulations☆32Updated 8 months ago
- A Python Toolkit for AGN Time Series Analysis using CARMA models☆18Updated 6 months ago
- Make a sweet giant triangle confusogram (GTC) plot☆34Updated last year
- Proximal Nested Sampling for high-dimensional Bayesian model selection☆23Updated 11 months ago
- Souce code for our ARA&A review of Gaussian process regression for astronomical time-series☆30Updated 3 weeks ago
- PHY 517 / AST 443 - Observational Techniques in Astronomy☆11Updated last week
- Materials related to AST1420 at the University of Toronto☆20Updated 11 months ago
- Some notes about MCMC☆44Updated 7 years ago
- Next generation Bayesian analysis tools for efficient posterior sampling and evidence estimation.☆29Updated 2 years ago
- Resources for Bayesian Models for Astrophysical Data - Hilbe, de Souza and Ishida, 2016, Cambridge University Press☆47Updated 2 years ago
- Probing the nature of dark matter by inferring the dark matter particle mass with machine learning and stellar streams.☆18Updated 2 years ago
- ☆13Updated 7 months ago
- Differentiable strong lens modelling on CPUs and GPUs☆26Updated 2 weeks ago
- A numpyro implementation of BayeSN, the optical-NIR SED model for type Ia supernovae☆12Updated 3 weeks ago
- 🖖 A package to determine whether planetary orbital configurations will live long and prosper☆64Updated last month
- DEPRECATED: use dfm/emcee version 3 instead☆21Updated 5 years ago
- Lecture notes on stellar physics, undergraduate level☆17Updated last year
- These are tutorials on how to use Pandas Data Frames for professional astronomy. Import common types of astronomical data, filter, sort,…☆11Updated 6 years ago
- Material for the "ML for the Sciences" meeting at Princeton University☆55Updated 5 years ago