hugobowne / bayesian-stats-simulation-tutorial
Bayesian statistical modelling using numpy and PyMC3. Telling stories using the language of probability. And more!
☆16Updated 5 years ago
Alternatives and similar repositories for bayesian-stats-simulation-tutorial:
Users that are interested in bayesian-stats-simulation-tutorial are comparing it to the libraries listed below
- Introduction to Statistical Modeling with Python (PyCon 2017)☆166Updated 4 years ago
- Notebooks for https://python-programming.quantecon.org☆54Updated last month
- Pandas tutorial for SciPy 2019☆101Updated last year
- PyData London 2019 Tutorial on Markov chain Monte Carlo with PyMC3☆155Updated 4 years ago
- My solutions to the assignments in the book: "A Student’s Guide to Bayesian Statistics" by Ben Lambert.☆64Updated 6 months ago
- ☆231Updated 3 years ago
- This is the course repository for w241 and 290 -- Experiments and Causality.☆16Updated 3 years ago
- Notebooks of Python and R code which illustrates basic causal inference using simulated data☆23Updated 5 years ago
- Tutorial given at PyData LA 2018☆97Updated 4 months ago
- An introduction to Bayesian statistics using Python and (coming soon) R.☆127Updated last year
- Explorations of survival analysis in Python☆51Updated last year
- Probabilistic Programming and Bayesian Computing with PyMC☆21Updated 6 months ago
- Repository for a workshop on Bayesian Decision Analysis☆69Updated last year
- Introduction to scikit-learn: Machine Learning in Python☆20Updated 2 years ago
- Presented at Scipy Conference 2019☆124Updated 4 years ago
- ☆100Updated 6 years ago
- Repository for an online class on Exploratory Data Analysis in Python☆66Updated 5 years ago
- Python port of "Common statistical tests are linear models" by Jonas Kristoffer Lindeløv.☆90Updated 4 months ago
- ☆39Updated 6 years ago
- Experimenting with and teaching probabilistic programming☆103Updated 2 years ago
- A collection of questions and solutions to problems presented at Rasmus Bååth's Bayesian probabilities workshop.☆123Updated 2 years ago
- There are always multiple ways to complete a task in Pandas. A minimal subset of the library is sufficient for almost everything.☆83Updated 2 years ago
- COMS W4995 Applied Machine Learning - Spring 20☆244Updated 2 years ago
- Preparation materials for CEAi Precision Workshop #1 on Bayesian modelling☆57Updated 6 years ago
- A collection of notebook to learn the Applied Predictive Modeling using Python.☆271Updated 8 years ago
- Using computational thinking to get deep insights on the foundations of linear algebra☆112Updated 3 months ago
- Added repo for PyData LA 2018 tutorial☆89Updated 6 years ago
- ☆108Updated 3 years ago
- Berlin Time Series Analysis Repository☆97Updated last year
- Exercises from 'Introduction to Statistical Learning with Applications in R' written in Python.☆104Updated 7 years ago