Computational Inference

2021 ◽  
pp. 39-60
Author(s):  
Nick Heard
Patterns ◽  
2021 ◽  
Vol 2 (2) ◽  
pp. 100176
Author(s):  
Hernando Santamaría-García ◽  
Sandra Baez ◽  
Diego Mauricio Aponte-Canencio ◽  
Guido Orlando Pasciarello ◽  
Patricio Andrés Donnelly-Kehoe ◽  
...  

2004 ◽  
Vol 101 (26) ◽  
pp. 9722-9727 ◽  
Author(s):  
B. Boussau ◽  
E. O. Karlberg ◽  
A. C. Frank ◽  
B.-A. Legault ◽  
S. G. E. Andersson

2015 ◽  
Vol 52 (02) ◽  
pp. 519-537 ◽  
Author(s):  
Jere Koskela ◽  
Paul Jenkins ◽  
Dario Spanò

Full likelihood inference under Kingman's coalescent is a computationally challenging problem to which importance sampling (IS) and the product of approximate conditionals (PAC) methods have been applied successfully. Both methods can be expressed in terms of families of intractable conditional sampling distributions (CSDs), and rely on principled approximations for accurate inference. Recently, more general Λ- and Ξ-coalescents have been observed to provide better modelling fits to some genetic data sets. We derive families of approximate CSDs for finite sites Λ- and Ξ-coalescents, and use them to obtain ‘approximately optimal’ IS and PAC algorithms for Λ-coalescents, yielding substantial gains in efficiency over existing methods.


2006 ◽  
Vol 22 (14) ◽  
pp. e497-e506 ◽  
Author(s):  
V. Varadan ◽  
D. M. Miller ◽  
D. Anastassiou

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