Xi'an's Og

This entry was posted on May 11, 2025 at 12:25 am and is filed under Books, Statistics, University life with tags 1⁰ North, Bayesian learning, book review, Cambridge University Press, continuous time MCMC, convergence diagnostics, cup, Gelman-Rubin statistic, Hamiltonian Monte Carlo, IMS Monographs, kernel Stein discrepancy descent, Markov chain Monte Carlo, MCMC, Metropolis adjusted Langevin algorithm, non-reversible MCMC, North, PDMP, piecewise deterministic, scalable Bayesian learning, scalable MCMC, stochastic differential equation, stochastic gradient MCMC. You can follow any responses to this entry through the RSS 2.0 feed. You can leave a response, or trackback from your own site.