= Radford M. Neal
{c}
<Radford M. Neal> works on <Bayesian inference>, <machine learning> and computational methods for complex <probability distributions>. His research page lists <neural networks>, <Gaussian processes> and <Markov chain Monte Carlo>.https://glizen.com/radfordneal/[Author research homepage]{ref}
His <MCMC Using Hamiltonian Dynamics by Radford M. Neal> develops <Hamiltonian Monte Carlo>: introduce auxiliary momenta, simulate approximately energy-preserving motion and correct discretization error with a <Metropolis–Hastings algorithm>. Longer, directed proposals can explore a target more effectively than a diffusive <random walk>. The combination of volume preservation, reversibility and an acceptance correction explains why an approximate numerical trajectory can still produce the intended invariant distribution.<MCMC Using Hamiltonian Dynamics by Radford M. Neal>[Sections 5.3.1–5.3.2]{ref}
\Image[https://glizen.com/radfordneal/pic-2009-07-HC.jpg]
{title=Radford M. Neal}
{description=Portrait linked from the author homepage.}
{source=https://glizen.com/radfordneal/}
{width=220}
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