Typically, one wants to infer which subpopulations are represented in a sample, and in what proportions. Here, we present a more flexible model in which each individual may be of pure or mixed ancestry. We use MCMC to compute the posterior probability of an individual's type of ancestry and the posterior distribution of associated genetic parameters. We demonstrate the method on data from wild populations which have been interbreeding with domestic strains. I compare three optimization algorithms on test data of a mixture of two populations where the true model parameters are known.
All algorithms do well for parameter estimation, but the estimates are biased.
All algorithms do well in three different methods to show the mixture is from two populations. One algorithm, evolutionary computation, allows for optimization on multiple criteria. I use this feature to reduce bias in the model parameter estimates. Bias reduction is possible for some parameters, but there is a limit to the number of parameters affected depending on the criteria used in the optimization. We hope to better determine age at sexual maturity and at first reproduction, life history parameters important to managing the population that are currently only known as functions of animal lengths.