Name
Abstract | ||
|---|---|---|
DNA sequence data are now being used to study the ancestral history of human population. The existing methods for such coalescence inference use recursion formula to compute the data probabilities. These methods are useful in practical applications, but computationally complicated. Here we first investigate the asymptotic behavior of such inference; results indicate that, broadly, the estimated coalescent time will be consistent to a finite limit. Then we study a relatively simple computation method for this analysis and illustrate how to use it. |
Year | DOI | Venue |
|---|---|---|
2014 | 10.1155/2014/194202 | COMPUTATIONAL AND MATHEMATICAL METHODS IN MEDICINE |
Keywords | Field | DocType |
computational biology,algorithms | Population,Coalescent theory,Applied mathematics,Inference,Computer science,Artificial intelligence,Statistics,Asymptotic analysis,Coalescence (physics),Recursion,Machine learning,Computation | Journal |
Volume | Issue | ISSN |
2014 | null | 1748-670X |
Citations | PageRank | References |
0 | 0.34 | 2 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Ao Yuan | 1 | 3 | 3.25 |
| Gengsheng Qin | 2 | 1 | 1.50 |
| Wenqing He | 3 | 7 | 4.36 |
| Qizhai Li | 4 | 17 | 3.81 |