Posts tagged pymc3.Data
Variational Inference: Bayesian Neural Networks
- 20 October 2021
- Category: intermediate
There are currently three big trends in machine learning: Probabilistic Programming, Deep Learning and “Big Data”. Inside of PP, a lot of innovation is in making things scale using Variational Inference. In this blog post, I will show how to use Variational Inference in PyMC3 to fit a simple Bayesian Neural Network. I will also discuss how bridging Probabilistic Programming and Deep Learning can open up very interesting avenues to explore in future research.
Getting started with PyMC3
- 30 August 2021
- Category: beginner
Authors: John Salvatier, Thomas V. Wiecki, Christopher Fonnesbeck
A Primer on Bayesian Methods for Multilevel Modeling
- 30 August 2021
- Category: intermediate
Hierarchical or multilevel modeling is a generalization of regression modeling. Multilevel models are regression models in which the constituent model parameters are given probability models. This implies that model parameters are allowed to vary by group. Observational units are often naturally clustered. Clustering induces dependence between observations, despite random sampling of clusters and random sampling within clusters.
A Hierarchical model for Rugby prediction
- 30 August 2021
- Category: intermediate
Based on the following blog post: Daniel Weitzenfeld’s, which based on the work of Baio and Blangiardo.