Markov chain Monte Carlo (MCMC) is an established approach for uncertainty quantification and propagation in scientific applications. A key challenge in applying MCMC to scientific domains is computation -- the target density of interest is often a …
Many probabilistic modeling problems in machine learning use gradient-based optimization in which the objective takes the form of an expectation. These problems can be challenging when the parameters to be optimized determine the probability …
Slice sampling is a Markov chain Monte Carlo algorithm for simulating samples from probability distributions, with the convenient property that it is rejection-free. When the slice endpoints are known, the sampling path is a deterministic function of …