no code implementations • 18 Feb 2024 • Marius Brusselmans, Luiz Max Carvalho, Samuel L. Hong, Jiansi Gao, Frederick A. Matsen IV, Andrew Rambaut, Philippe Lemey, Marc A. Suchard, Gytis Dudas, Guy Baele
Modern phylogenetics research is often performed within a Bayesian framework, using sampling algorithms such as Markov chain Monte Carlo (MCMC) to approximate the posterior distribution.
1 code implementation • 24 Feb 2019 • Mingwei Tang, Gytis Dudas, Trevor Bedford, Vladimir N. Minin
We propose a Bayesian model that combines phylodynamic inference and stochastic epidemic models, and achieves computational tractability by using a linear noise approximation (LNA) --- a technique that allows us to approximate probability densities of stochastic epidemic model trajectories.
