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Found 3 papers, 1 papers with code
Stochastic Approximation with Biased MCMC for Expectation Maximization
In practice, MCMC-SAEM is often run with asymptotically biased MCMC, for which the consequences are theoretically less understood.
On the convergence of dynamic implementations of Hamiltonian Monte Carlo and No U-Turn Samplers
Under conditions similar to the ones existing for HMC, we also show that NUTS is geometrically ergodic.
Learning Riemannian metric for disease progression modeling
Specifically, we learn the metric as the push-forward of the Euclidean metric by a diffeomorphism.
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