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Approximate Bayesian computation vs Markov chain Monte Carlo

Friday 15th December 2006 - 10:00 to 10:45
Center for Mathematical Sciences

Approximate Bayesian Computation (ABC) is a recent developed Bayesian technique that can be used to extract information from DNA data. This method has been firstly introduced to Population Genetics in 1997 by Pritchard.

Since 2002, with Beaumont’s paper on the subject, its usage has been strongly increased. This Bayesian approach is used to estimate several demographic history parameters, from populations, using DNA data. Its main advantages are the decrease on computation time demanding and the increase on efficiency and flexibility when dealing with multiparameter models.

In this project it has been studied a particular ABC method similar to the one used by Beaumont in 2006, against a commonly used Markov Chain Monte Carlo (MCMC) method (Hey and Nielsen, 2004) to infer about the accuracy of the first method. It was also explored the use of this method with more complex demographic models. These two approaches use DNA sequence data to extract demographic information (e.g. population sizes, time of splitting events, migration rates).

The study confirms the competitiveness of this method when compared to an MCMC approach as well as its potential role on researches with more complex, therefore more realistic, models.

Related Links

University of Cambridge Research Councils UK
    Clay Mathematics Institute London Mathematical Society NM Rothschild and Sons