Sammenlign metoder

	Nettverksbasert fylogenetisk analyse	Bayesiansk fylogenetisk analyse
Fagfelt	Bioinformatikk	Bioinformatikk
Familie	Process / pipeline	Process / pipeline
Opprinnelsesår	1992–2004 (foundational algorithms); broader development 1990s–2010s	1996–2001
Opphavsperson	Hans-Jürgen Bandelt & Andreas Dress (split decomposition); David Bryant & Vincent Moulton (Neighbor-Net)	Rannala & Yang (1996); operationalized by Huelsenbeck et al. (MrBayes, 2001)
Type	Computational phylogenetic method	Probabilistic inference method
Opprinnelig kilde	Bandelt, H.-J., & Dress, A. W. M. (1992). Split decomposition: A new and useful approach to phylogenetic analysis of distance data. Molecular Phylogenetics and Evolution, 1(3), 242–252.	Ronquist, F., & Huelsenbeck, J. P. (2003). MrBayes 3: Bayesian phylogenetic inference under mixed models. Bioinformatics, 19(12), 1572–1574.
Alias	phylogenetic network, reticulate phylogenetics, split network analysis, evolutionary network inference	Bayesian phylogenetics, Bayesian inference of phylogeny, MCMC phylogenetics, Bayesian molecular phylogenetics
Relaterte	6	3
Sammendrag	Network-based phylogenetic analysis constructs graph-structured representations of evolutionary relationships that explicitly accommodate reticulate events — including hybridization, horizontal gene transfer, recombination, and incomplete lineage sorting — which strictly bifurcating phylogenetic trees cannot represent. Instead of forcing sequences into a single bifurcating tree, the method infers splits or reticulations in the data and visualises them as a network, revealing conflicting phylogenetic signals that are biologically informative.	Bayesian phylogenetic analysis uses Bayes' theorem and Markov chain Monte Carlo (MCMC) sampling to estimate the posterior probability distribution over phylogenetic trees and model parameters given observed sequence data. Unlike bootstrapped maximum-likelihood methods that return a single best tree, Bayesian inference yields a credible set of trees with associated posterior probabilities, providing a principled measure of phylogenetic uncertainty. It is the dominant framework for estimating divergence times and ancestral relationships in molecular evolution.