Skip to content

RJTK/stanlearn

Folders and files

NameName
Last commit message
Last commit date

Latest commit

 

History

83 Commits
 
 
 
 
 
 
 
 
 
 

Repository files navigation

I have taken up this project for the purposes of learning a bit more about Bayesian statistics and how to program with Stan. I've bulit up some simple classes and idioms to hopefully make it fairly straightforward to write a stan model and then interface with it via the sklearn API -- this is quite convenient for experimenting with different datasets.

The first model I implemented is simply a linear regression model with T-distributed output

  y0 ~ cauchy(0, 1);
  nu ~ cauchy(0, 1);
  sigma ~ normal(0, 1);  // half-normal
  lam ~ exponential(1);
  theta ~ normal(0, lam);
  y ~ student_t(nu, y0 + Q * theta, sigma);

the input data needs to be scaled to unit variance before feeding it in. I obviously do not claim that the following examples are exemplars of remarkable performance -- they are illustrations.

Here's some examples on the Diabetes dataset. Parameter (marginal) posteriors:

alt tag

Out of sample predictions with uncertainty quantification (Linear Regression)

alt tag

Another interesting model is VAR(p) models. So far, I have developed an AR(p) model, with a parameterization in terms of the reflection coefficients (see /RJTK/levinson-durbin-recursion or any book on signal processing). This guarantees the stability of every posterior sample.

alt tag

alt tag

About

Implementation of some Bayesian ML algorithms in Stan with an sklearn-like interface.

Resources

License

Stars

Watchers

Forks

Releases

No releases published

Packages

No packages published