GPU Posterior Simulation - Bayesian Simulated Annealing - Quantum Annealing




  1. Choose a SABL-Project - download the .zip archive, and extract the directory contained inside it.
  2. Switch to the directory that was just extracted and start Matlab
  3. Execute: run.m

Full installation

Download SABL (Installation instructions are contained in the archive .zip file.)

This is the original release of SABL (Sequentially Adaptive Bayesian Learning) resulting from Australian Research Council Grant DP130103356 (Massively parallel algorithms for Bayesian inference and decision making). Details of the research project and the original team are here.

The aim of SABL-Projects

The aim of this site is simple:

To collect real-use examples of SABL from all application domains for the benefit of new users

These examples supplement the original guide written by Professor John Geweke in 2015. A new SABL user (with a moderate Matlab programming background) can then learn by example, taking code snippets that will be useful in their own application for such tasks as:


Simple generic SABL project

Concepts covered

Download Installation
Link Expand the .zip, then from the extracted directory, start Matlab and execute run.m

The Impact of Confirmation Bias on Willingness To Pay for Financial Advice

View output

Concepts covered

All items listed above

Download Installation
Link Expand the .zip contents to a new subdirectory under the “Projects” directory of your SABL installation.

Roadmap to Quantum Computing

An early implementation of quantum computing is underway thanks to D-Wave. Where SABL performs unconstrained optimisation on GPUs for real-valued problems, D-Wave performs unconstrained optimisation on qubit hardware for (quadratic) integer-valued problems. Future SABL projects will look to bridge these two through the qbsolv software released by D-Wave.

Contact Us

Please submit your thoughts and suggestions as Issues at this GitHub site.