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Found 4 papers, 0 papers with code
Mini-batch Metropolis-Hastings MCMC with Reversible SGLD Proposal
To further extend the utility of the algorithm to high dimensional settings, we construct a proposal with forward and reverse moves using stochastic gradient and show that the construction leads to reasonable acceptance probabilities.
Convergence of Contrastive Divergence with Annealed Learning Rate in Exponential Family
In this paper, we establish consistency and convergence rate of CD with annealed learning rate $\eta_t$.
Convergence of Contrastive Divergence Algorithm in Exponential Family
\sum_{s=0}^{t-1} \theta_s \right/ t$ as $t \to \infty$ is a consistent estimate for the true parameter $\theta_\star$.
Learning Summary Statistic for Approximate Bayesian Computation via Deep Neural Network
Approximate Bayesian Computation (ABC) methods are used to approximate posterior distributions in models with unknown or computationally intractable likelihoods.
