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Found 5 papers, 1 papers with code
Structure Learning with Adaptive Random Neighborhood Informed MCMC
In this paper, we introduce a novel MCMC sampler, PARNI-DAG, for a fully-Bayesian approach to the problem of structure learning under observational data.
Optimal design of the Barker proposal and other locally-balanced Metropolis-Hastings algorithms
We derive an optimal choice of noise distribution for the Barker proposal, optimal choice of balancing function under a Gaussian noise distribution, and optimal choice of first-order locally-balanced algorithm among the entire class, which turns out to depend on the specific target distribution.
A fresh take on 'Barker dynamics' for MCMC
We provide a full derivation of the method from first principles, placing it within a wider class of continuous-time Markov jump processes.
Computation Methodology
On the Geometric Ergodicity of Hamiltonian Monte Carlo
We establish general conditions under which Markov chains produced by the Hamiltonian Monte Carlo method will and will not be geometrically ergodic.
Gradient-free Hamiltonian Monte Carlo with Efficient Kernel Exponential Families
We propose Kernel Hamiltonian Monte Carlo (KMC), a gradient-free adaptive MCMC algorithm based on Hamiltonian Monte Carlo (HMC).
