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#55 Phylogenetics and the likelihood gradient with Xiang Ji
Manage episode 282181397 series 1537951
In this episode, we chat about phylogenetics with Xiang Ji. We start with a general introduction to the field and then go deeper into the likelihood-based methods (maximum likelihood and Bayesian inference). In particular, we talk about the different ways to calculate the likelihood gradient, including a linear-time exact gradient algorithm recently published by Xiang and his colleagues.
Links:
- Gradients Do Grow on Trees: A Linear-Time O(N)-Dimensional Gradient for Statistical Phylogenetics (Xiang Ji, Zhenyu Zhang, Andrew Holbrook, Akihiko Nishimura, Guy Baele, Andrew Rambaut, Philippe Lemey, Marc A Suchard)
- BEAGLE: the package that implements the gradient algorithm
- BEAST: the program that implements the Hamiltonian Monte Carlo sampler and the molecular clock models
If you enjoyed this episode, please consider supporting the podcast on Patreon.
70 episodes
Manage episode 282181397 series 1537951
In this episode, we chat about phylogenetics with Xiang Ji. We start with a general introduction to the field and then go deeper into the likelihood-based methods (maximum likelihood and Bayesian inference). In particular, we talk about the different ways to calculate the likelihood gradient, including a linear-time exact gradient algorithm recently published by Xiang and his colleagues.
Links:
- Gradients Do Grow on Trees: A Linear-Time O(N)-Dimensional Gradient for Statistical Phylogenetics (Xiang Ji, Zhenyu Zhang, Andrew Holbrook, Akihiko Nishimura, Guy Baele, Andrew Rambaut, Philippe Lemey, Marc A Suchard)
- BEAGLE: the package that implements the gradient algorithm
- BEAST: the program that implements the Hamiltonian Monte Carlo sampler and the molecular clock models
If you enjoyed this episode, please consider supporting the podcast on Patreon.
70 episodes
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