STATS 215: Statistical Models in Biology
Master/PhD course, Stanford University, Department of Statistics
Winter 2020
Course description
This course is about probabilistic models in biology and the statistical inference algorithms necessary to fit them to data. We will cover some of the most important tools for modeling biological data, including latent variable models, hidden Markov models, dynamical systems, Poisson processes, and recent extensions like variational autoencoders and recurrent neural networks. We will study algorithms for parameter estimation and inference, and we will apply these tools to a variety of problems across biology, with a particular emphasis on applications in neuroscience. In your homework assignments and final project, you will implement these models and algorithms and apply them to real data.