* A simple and efficient algorithm for generating random variates from the class of Lipschitz continuous densities is described. A MatLab implementation is freely available on GitHub.*

# Category: Exposition

# Bayesian learning

**Friday july 28 at 17:00**

**Rutherford Physics Building, Room 118, McGill**

Next week, I’ll be talking about *Bayesian learning* at the Mathematical congress of the americas and at the Canadian undergraduate mathematics conference. These are somewhat challenging talks: I need to sell the idea of Bayesian statistics to a general mathematical audience (which knows nothing about it), interest them in some though problems of Bayesian nonparametrics, and then present some of our research results. This must be done in under 20 minutes.

To make the presentation more intuitive and accessible, I borrowed some language from machine learning. I’m talking about learning rather than inference, uncertain knowledge rather than subjective belief, and “asymptotic correctness” rather than consistency. These are essentially synonymous, although some authors might use them in different ways. This should not cause problems for this introductory talk.Read More »