Fixed-Size Determinantal Point Processes Sampling For Species Phylogeny

Determinantal point processes (DPPs) are popular tools that supply useful information for repulsiveness. They provide coherent probabilistic models when negative correlations arise and also represent new algorithms inference problems like sampling, marginalization conditioning. Recently, DPPs have played an increasingly important role in machine learning statistics, since they used diverse subset selection problems. In this paper we use k-DPP, a conditional DPP only sets of cardinality k, to sample species from large phylogenetic tree. The tree sampling task is many studies modern bioinformatics. results show fast mixing sampler which polynomial bound on the time given. This approach applied real-world dataset species, observe leaves joined by higher subtree more likely appear.