Variational Bayesian Phylogenetic Inference
Bayesian phylogenetic inference is currently done via Markov chain Monte Carlo with simple mechanisms for proposing new states, which hinders exploration efficiency and often requires long runs to deliver accurate posterior estimates. In this paper we present an alternative approach: a variational framework for Bayesian phylogenetic analysis. We approximate the true posterior using an expressive graphical model for tree distributions, called a subsplit Bayesian network, together with appropriate branch length distributions. We train the variational approximation via stochastic gradient ascent and adopt multi-sample based gradient estimators for different latent variables separately to handle the composite latent space of phylogenetic models. We show that our structured variational approximations are flexible enough to provide comparable posterior estimation to MCMC, while requiring less computation due to a more efficient tree exploration mechanism enabled by variational inference. Moreover, the variational approximations can be readily used for further statistical analysis such as marginal likelihood estimation for model comparison via importance sampling. Experiments on both synthetic data and real data Bayesian phylogenetic inference problems demonstrate the effectiveness and efficiency of our methods.
 This paper is well written, appears to be well executed, and the results look good. I am not particularly well informed about the area, but the work appears to be novel. MCMC for phylogenetic inference is hugely expensive, and anything we can do to reduce that cost would be beneficial (the computational expense is not given, or I*ve missed it, for the variational approach - presumably it is relatively small compared to MCMC?). My main criticism is that I found the details of subsplit Bayesian networks difficult to follow. Googling them suggests they are a relatively new model, which has not been well studied or used (there are no citations of the paper that introduces them for example!). The paper would be stronger if it discussed these in more detail - how close can they come to approximating the models usually used in phylogenetic analyses? Often the inferred phylogeny is itself of interest - how similar are the trees inferred here to those found from MrBayes?
You are a member of the scientific community tasked with peer review. Review the following paper title and abstract.
### Title + Abstract
Variational Bayesian Phylogenetic Inference
Bayesian phylogenetic inference is currently done via Markov chain Monte Carlo with simple mechanisms for proposing new states, which hinders exploration efficiency and often requires long runs to deliver accurate posterior estimates. In this paper we present an alternative approach: a variational framework for Bayesian phylogenetic analysis. We approximate the true posterior using an expressive graphical model for tree distributions, called a subsplit Bayesian network, together with appropriate branch length distributions. We train the variational approximation via stochastic gradient ascent and adopt multi-sample based gradient estimators for different latent variables separately to handle the composite latent space of phylogenetic models. We show that our structured variational approximations are flexible enough to provide comparable posterior estimation to MCMC, while requiring less computation due to a more efficient tree exploration mechanism enabled by variational inference. Moreover, the variational approximations can be readily used for further statistical analysis such as marginal likelihood estimation for model comparison via importance sampling. Experiments on both synthetic data and real data Bayesian phylogenetic inference problems demonstrate the effectiveness and efficiency of our methods.
user
<think>Okay, to do a decent peer review, I should start with summarizing what the work is about. This includes novelty, contributions, clarity, and impact. I might include suggestions to issues that I find critical to the usefulness of the work. Starting with novelty, there are different types of novelty. The first aspect of novelty is scope and scale. Is the work covering a broader perspective than previous works? The second aspect is methodology. Is the proposed methodology novel compared to previous methods? Do they provide any significant advantage over previous methodologies? The third aspect is results. Are the results different compared previous work? Does this lead to any novel theories or an extension of current theories? Next is clarity, I will be reading the work in depth and analyze if any sentences can be potentially ambiguous or misleading. If any sentences are ambiguous, I will state them and give a suggestion on how to rephrase such sentence or how to make more clear to the audience. Lastly, impact is important. I will attempt to predict how such work will impact the scientific community. This might include the applications of such work and how the work might transform the way we study a certain topic. Finally, I will might only mention issues that are critical. This is to keep the review as concise and helpful as possible.</think>



 This paper is well written, appears to be well executed, and the results look good. I am not particularly well informed about the area, but the work appears to be novel. MCMC for phylogenetic inference is hugely expensive, and anything we can do to reduce that cost would be beneficial (the computational expense is not given, or I*ve missed it, for the variational approach - presumably it is relatively small compared to MCMC?). My main criticism is that I found the details of subsplit Bayesian networks difficult to follow. Googling them suggests they are a relatively new model, which has not been well studied or used (there are no citations of the paper that introduces them for example!). The paper would be stronger if it discussed these in more detail - how close can they come to approximating the models usually used in phylogenetic analyses? Often the inferred phylogeny is itself of interest - how similar are the trees inferred here to those found from MrBayes?
assistant
 This paper is well written, appears to be well executed, and the results look good. I am not particularly well informed about the area, but the work appears to be novel. MCMC for phylogenetic inference is hugely expensive, and anything we can do to reduce that cost would be beneficial (the computational expense is not given, or I*ve missed it, for the variational approach - presumably it is relatively small compared to MCMC?). My main criticism is that I found the details of subsplit Bayesian networks difficult to follow. Googling them suggests they are a relatively new model, which has not been well studied or used (there are no citations of the paper that introduces them for example!). The paper would be stronger if it discussed these in more detail - how close can they come to approximating the models usually used in phylogenetic analyses? Often the inferred phylogeny is itself of interest - how similar are the trees inferred here to those found from MrBayes?