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 the modelling of 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.Comment: Accepted as a full paper for publication at RECOMB-CG 2023
(Camera-ready version). 15 pages (excluding references), 6 tables and 1
figur