Simulation of an extension of Mallows-Bradley-Terry ranking model by acceptance-rejection method

This paper is concerned with the simulation of an extension of the Mallows-Bradley-Terry ranking probability model by the acceptance-rejection method. A Monte Carlo Markov Chain (MCMC) algorithm for the simulation of the model has been already proposed when the number q of items to be ranked is large, say more than 7. However, in most real life situations the number q of items to be ranked does not exceed 10, e.g., psycho physics, food testing, etc. Therefore, the proposed tool relies on appropriate choice of the constant and instrumental distribution by means of the well-known acceptance-rejection method to generate samples from the target distribution.

Automated summary

This paper focuses on the simulation of an extended Mallows-Bradley-Terry ranking probability model using the acceptance-rejection method. While a Monte Carlo Markov Chain (MCMC) algorithm has been proposed for large q values, the proposed tool emphasizes the importance of appropriate constant and instrumental distribution selection for generating samples from the target distribution, especially when the number of items to be ranked is small.