Prior Density Learning in Variational Bayesian Phylogenetic Parameters Inference
0301 basic medicine
FOS: Computer and information sciences
Computer Science - Machine Learning
0303 health sciences
03 medical and health sciences
FOS: Biological sciences
Populations and Evolution (q-bio.PE)
Quantitative Biology - Populations and Evolution
Machine Learning (cs.LG)
DOI:
10.48550/arxiv.2302.02522
Publication Date:
2023-01-01
AUTHORS (3)
ABSTRACT
Accepted as a full paper for publication at RECOMB-CG 2023 (LNBI proof version). 15 pages (excluding references), 6 tables and 1 figure<br/>The advances in variational inference are providing promising paths in Bayesian estimation problems. These advances make variational phylogenetic inference an alternative approach to Markov Chain Monte Carlo methods for approximating the phylogenetic posterior. However, one of the main drawbacks of such approaches is modelling the prior through fixed distributions, which could bias the posterior approximation if they are distant from the current data distribution. In this paper, we propose an approach and an implementation framework to relax the rigidity of the prior densities by learning their parameters using a gradient-based method and a neural network-based parameterization. We applied this approach for branch lengths and evolutionary parameters estimation under several Markov chain substitution models. The results of performed simulations show that the approach is powerful in estimating branch lengths and evolutionary model parameters. They also show that a flexible prior model could provide better results than a predefined prior model. Finally, the results highlight that using neural networks improves the initialization of the optimization of the prior density parameters.<br/>
SUPPLEMENTAL MATERIAL
Coming soon ....
REFERENCES ()
CITATIONS ()
EXTERNAL LINKS
PlumX Metrics
RECOMMENDATIONS
FAIR ASSESSMENT
Coming soon ....
JUPYTER LAB
Coming soon ....