Deep Learning for Population Genetic Inference

Deep Learning for Population Genetic Inference

Given genomic variation data from multiple individuals, computing the likelihood of complex population genetic models is often infeasible. To circumvent this problem, we introduce a novel likelihood-free inference framework by applying deep learning, a powerful modern technique in machine learning. Deep learning makes use of multilayer neural networks to learn a feature-based function from the ...

Deep Learning for Causal Inference

In this paper, we propose the use of deep learning techniques in econometrics, specifically for causal inference and for estimating individual as well as average treatment effects. The contribution of this paper is twofold: 1.For generalized neighbor matching to estimate individual and average treatment effects, we analyze the use of autoencoders for dimensionality reduction while maintaining t...

Learning Deep Inference Machines

Introduction. The traditional approach to structured prediction problems is to craft a graphical model structure, learn parameters for the model, and perform inference using an efficient– and usually approximate– inference approach, including, e.g., graph cut methods, belief propagation, and variational methods. Unfortunately, while remarkably powerful methods for inference have been developed ...

Population Genetic Inference

Members of the Department of Statistics will be teaching a graduate-level course of eight lectures introducing the theory and techniques of population genetic inference with particular emphasis on modern developments in the field of human genetics. Lectures of 90 minutes will be held at 2pm on Tuesdays in Lecture Theatre C in the Department of Zoology on South Parks Road. No registration is req...

Principles of population genetic inference