Markov Chain Monte Carlo Simulation Algorithms for quick convergence solution using solution engine of IBM SPSS Modeler - BC-438

Preferred Disciplines: Mathematical (Master, PhD or Post-Doc level)
Project length: 4-12 months
Approx. start date: As soon as possible
Location: Vanderhoof, BC 
No. of Positions: 1
Preferences: University of Northern BC, Thompson Rivers University
Company: Anonymous

About Company:

Partner is IBM Software Business Partner in Canada.

Summary of Project:

The project focuses on creating an API in Python that when compiled can use “hooks” within IBM SPSS Modeler to solve a modeling problem that uses Markov (or Semi-Markov) Chain Monte Carlo (MCMC) algorithm. The research objectives are to create sample problems and solutions that utilize MCMC, evaluate the two industry standard algorithms with existing limitations, write a Python language program for solution and compile an API that contains a data handler that interfaces with IBM SPSS Modeler for solution and visualization of obtained results.

Research Objectives/Sub-Objectives:

  • Sample problems and solutions utilising MCMC
  • Evaluate existing industry standard algorithms
  • Compile and data handling API written in Python language
  • Interface with IBM SPSS Modeler engine for solution


    • Categaorization and Evaluation of existing algorithms
    • Analysis of error bounds of current algorithms
    • Unknown methodology to be documented after research program

    Expertise and Skills Needed:

    • Minimum M.Sc. level in mathematical Statistics
    • Fluency in Python in Eclipse Integrated Deevelopment Environment (IDE)  

    For more info or to apply to this applied research position, please

    1. Check your eligibility and find more information about open projects.
    2. Interested students need to get the approval from their supervisor and send their CV along with a link to their supervisor’s university webpage by applying through the webform