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pro vyhledávání: '"Gruffaz, Samuel"'
The expectation maximization (EM) algorithm is a widespread method for empirical Bayesian inference, but its expectation step (E-step) is often intractable. Employing a stochastic approximation scheme with Markov chain Monte Carlo (MCMC) can circumve
Externí odkaz:
http://arxiv.org/abs/2402.17870
We propose a theoretically justified and practically applicable slice sampling based Markov chain Monte Carlo (MCMC) method for approximate sampling from probability measures on Riemannian manifolds. The latter naturally arise as posterior distributi
Externí odkaz:
http://arxiv.org/abs/2312.00417
There is substantial empirical evidence about the success of dynamic implementations of Hamiltonian Monte Carlo (HMC), such as the No U-Turn Sampler (NUTS), in many challenging inference problems but theoretical results about their behavior are scarc
Externí odkaz:
http://arxiv.org/abs/2307.03460