Approximate Bayesian Inference

• Andere auteurs: Alquier, Pierre (Redacteur)
• Formaat: Elektronisch Hoofdstuk
• Taal: Engels
• Gepubliceerd in: Basel MDPI - Multidisciplinary Digital Publishing Institute 2022
• Onderwerpen: Research & information: general; Mathematics & science; 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; no free lunch theorems; sequential learning; principal curves; data streams; regret bounds; greedy algorithm; sleeping experts; entropy; robustness; statistical mechanics; complex systems
• Online toegang: DOAB: download the publication; DOAB: description of the publication

Extremely popular for statistical inference, Bayesian methods are also becoming popular in machine learning and artificial intelligence problems. Bayesian estimators are often implemented by Monte Carlo methods, such as the Metropolis-Hastings algorithm of the Gibbs sampler. These algorithms target...