Coalescent-based, maximum likelihood inference in phylogeography

Fully coalescent-based approaches to phylogeography

Last time we saw an early example of using coalescent theory to distinguish between two scenarios describing the history of populations. In the example we considered, Knowles [7] compared two scenarios, the “widespread ancestor” and the “multiple glacial refugia” scenarios. To make the comparison she simulated data under the “widespread ancestor” hypothesis, collected the samples into a multipl...

Targeted Maximum Likelihood Based Causal Inference: Part II

In this article, we provide a template for the practical implementation of the targeted maximum likelihood estimator for analyzing causal effects of multiple time point interventions, for which the methodology was developed and presented in Part I. In addition, the application of this template is demonstrated in two important estimation problems: estimation of the effect of individualized treat...

Targeted maximum likelihood based causal inference: Part I.

Given causal graph assumptions, intervention-specific counterfactual distributions of the data can be defined by the so called G-computation formula, which is obtained by carrying out these interventions on the likelihood of the data factorized according to the causal graph. The obtained G-computation formula represents the counterfactual distribution the data would have had if this interventio...

Local Maximum Likelihood Estimation and Inference

Maximum Likelihood Methods for Phylogenetic Inference

In this article, we provide an overview of maximum likelihood methods for phylogenetic inference. A brief introduction to general maximum likelihood estimation is provided. We define a phylogenetic likelihood, summarize how to compute this likelihood, and then discuss approaches used to maximize the phylogenetic likelihood function. We discuss a property of the maximum likelihood estimation, ca...