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Palabras clave – provistas por la editorial

bifurcation; dynamical systems; Edward–Sokal coupling; mean-field; Kullback–Leibler divergence; variational inference; Bayesian statistics; machine learning; variational approximations; PAC-Bayes; expectation-propagation; Markov chain Monte Carlo; Langevin Monte Carlo; sequential Monte Carlo; Laplace approximations; approximate Bayesian computation; Gibbs posterior; MCMC; stochastic gradients; neural networks; Approximate Bayesian Computation; differential evolution; Markov kernels; discrete state space; ergodicity; Markov chain; probably approximately correct; variational Bayes; Bayesian inference; Markov Chain Monte Carlo; Sequential Monte Carlo; Riemann Manifold Hamiltonian Monte Carlo; integrated nested laplace approximation; fixed-form variational Bayes; stochastic volatility; network modeling; network variability; Stiefel manifold; MCMC-SAEM; data imputation; Bethe free energy; factor graphs; message passing; variational free energy; variational message passing; approximate Bayesian computation (ABC); differential privacy (DP); sparse vector technique (SVT); Gaussian; particle flow; variable flow; Langevin dynamics; Hamilton Monte Carlo; non-reversible dynamics; control variates; thinning; meta-learning; hyperparameters; priors; online learning; online optimization; gradient descent; statistical learning theory; PAC–Bayes theory; deep learning; generalisation bounds; Bayesian sampling; Monte Carlo integration; PAC-Bayes theory; no free lunch theorems; sequential learning; principal curves; data streams; regret bounds; greedy algorithm; sleeping experts; entropy; robustness; statistical mechanics; complex systems