There are 2 repositories under pystan topic.
A Python package for Bayesian forecasting with object-oriented design and probabilistic models under the hood.
Python/STAN Implementation of Multiplicative Marketing Mix Model, with deep dive into Adstock (carry-over effect), ROAS, and mROAS
Personal project to compare hierarchical linear regression in PyMC3 and PyStan, as presented at http://pydata.org/london2016/schedule/presentation/30/ video: https://www.youtube.com/watch?v=Jb9eklfbDyg
My solutions to the exercises in "Data Analysis Using Regression and Multilevel/Hierarchical Models" by Andrew Gelman and Jennifer Hill
(ml) - python implementation of bayesian media mix modelling with shape and carryover effect
Basic statistical modelling examples.
Source code and data for the EDM 2022 paper
spatial_attenNCM (Spatial Attention Neuro-Cognitive Modeling) used some hierarchical neuro-cognitive models to find out the spatial attention effect on perceptual decision making.
Self-study materials to give you an introduction to Bayesian inference with python
It is from Kaggle Competitions where the training dataset is very small and the testing dataset is very large and we have to avoid or reduce overfiting by looking for best possible ways to overcome the most popular problem faced in field of predictive analytics.
"Probabilistic Programming & Bayesian Methods for Hackers" book ported to Stan (python)
estimate competitive programmers' performance based on Bayesian statistical modeling
Probabilistic modeling through Bayesian inference using PyStan with demonstrative case study experiments from Christopher Bishop's Model-based Machine Learning.
Replica Exchange Monte Carlo using PyStan2
Notebook to study Bayesian statistical modeling with pystan and "StanとRでベイズ統計モデリング"
A comparison of basic models written in pystan vs pymc3
Forecasting Net Prophet
Detecting unobservable changes in standard deviation of GDP
Predicting CO2 levels using a Bayesian inference model.
Modeling a 1-armed bandit with pystan.
Repository for https://qiita.com/akeyhero/items/894dd3b5c206325191ce [Japanese]
Files for running PyStan on Binder
Learning probabilistic modeling in python
Estos son algunos ejemplos de práctica que se han hecho en algún mometo.
Module 11 - I will be creating a visual depiction of seasonality (as measured by Google Search traffic), an evaluation of how the company stock price correlates to Google Search traffic, A Prophet forecast model that can predict hourly user search traffic, and a plot of a forecast for the company’s future revenue.