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.[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.[ref]
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Radford M. Neal is a prominent statistician and researcher known for his work in Bayesian statistics, machine learning, and computational methods. He is a professor at the University of Toronto and has made significant contributions to the development of algorithms for Bayesian inference, including Markov Chain Monte Carlo (MCMC) methods, such as the Hamiltonian Monte Carlo (HMC) method. Neal is also known for his work on Gaussian processes and other probabilistic models.