Markov chain Monte Carlo

…and Bayesian inference, including Markov chain Monte Carlo, and study Brownian motion, continuous-time Markov chains and birth–death processes. These tools are applied to trait evolution, biogeography and diversification, including the effects of environmental and biotic interactions. The practical course is intended for researchers familiar with programming, probability and phylogenetics; prior Julia experience is not required. Sessions on 1, 3, 5, 8, 10 and 12 March 2

…variability, we leverage a Markov chain Monte Carlo method to generate populations of electrical models that can reproduce the variability

The activity of 100s-1000’s of neurophysiological signals can be recorded during behaviors and in response to sensory stimuli, creating high-dimensional time-series data. Understanding such data is often aided by methods that extract low-dimensional latent structure present in the high-dimensional recordings. Multi-output Gaussian process models leverage the nonparametric nature of Gaussian processes to capture structure across multiple outputs. However, this class of models typically assumes that the correlations between the output response variables are invariant in the input space (e.g., se