This repository contains our semester end term project for the subject STATISTICAL COMPUTING with R & PYTHON.
We were intrigued by the Monte Carlo Markov chain and Metropolis algorithm and wanted to explore more as they are highly applied in our domain field of Industrial Engineering. As this course on statistical computing, we wanted to explore the statistics part of the concept and go a step ahead to see the advances in this field and their present applications.
We came across RAM - Repelling Attracting Metropolis Algorithm when we were searching for methods to create mutlimodal samples.
- Understand and implement the Repelling Attracting Metropolis Algorithm developed by Hyungsuk Tak, Xiao-Li Meng, David A. van Dyk
- Apply RAM on sensor network localization
- Compare computational time and speed of RAM with BFGS(an optimization technique) - This step is done to apply the lessons we learned in the class
- Apply RAM on real dataset - 7-dimensional posterior form an EXOPLANET detection problem to populate samples and find the planet's orbital period
Randomly and simulated data from the techniques learned in the class
The fitting code is written in R.