**Date/Time**

Date(s) - 01/02/2023*12:30 pm - 2:00 pm*

**Abstract: **Biologists often want to understand why certain traits of organisms evolved. One way of translating this question into a statistical context is to set up a continuous-time, discrete-state Markov process along the branches of a phylogenetic tree and estimate how the evolutionary rates (log-hazards of changes in particular states) vary depending on the current values of other traits. Using an example from fish biology I will explain how to estimate the log-likelihood of a set of tip states given a transition matrix; based on this computation we can use standard MCMC techniques to estimate linear contrasts that describe the effects of one biological trait on the evolutionary rate of another trait. I hope to (1) rant briefly about some conceptual issues, in particular why single, scalar metrics describing model performance (in this case, p-values, but applicable to AUC/AIC/etc.) are less useful than interval measures of uncertainty (2) discuss some future technical improvements using automatic differentiation to enable Hamilton Monte Carlo sampling.

**Location:** DSB AB103 & Zoom

**Zoom Meeting Info:**

Meeting ID: 993 2631 5722

Passcode: 913408

https://mcmaster.zoom.us/j/99326315722?pwd=YVpYVW8yUHpyaU5RTmdjNTI4WGlPUT09