Posts in intermediate
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.
GLM: Mini-batch ADVI on hierarchical regression model
- 23 September 2021
- Category: intermediate
Unlike Gaussian mixture models, (hierarchical) regression models have independent variables. These variables affect the likelihood function, but are not random variables. When using mini-batch, we should take care of that.
Probabilistic Matrix Factorization for Making Personalized Recommendations
- 20 September 2021
- Category: intermediate
So you are browsing for something to watch on Netflix and just not liking the suggestions. You just know you can do better. All you need to do is collect some ratings data from yourself and friends and build a recommendation algorithm. This notebook will guide you in doing just that!
Marginalized Gaussian Mixture Model
- 18 September 2021
- Category: intermediate
Gaussian mixtures are a flexible class of models for data that exhibits subpopulation heterogeneity. A toy example of such a data set is shown below.
Gaussian Mixture Model
- 18 September 2021
- Category: intermediate
Original NB by Abe Flaxman, modified by Thomas Wiecki
Rolling Regression
- 15 September 2021
- Category: intermediate
Pairs trading is a famous technique in algorithmic trading that plays two stocks against each other.
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.