Approximate Bayesian Inference
Alquier, Pierre (editor)
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 the exact posterior distribution. However, many of the modern models in statistics are simply too complex to use such methodologies. In machine learning, the volume of the data used in practice makes Monte Carlo methods too slow to be useful. On the other hand, these applications often do not require an exact knowledge of the posterior. This has motivated the development of a new generation of algorithms that are fast enough to handle huge datasets but that often target an approximation of the posterior. This book gathers 18 research papers written by Approximate Bayesian Inference specialists and provides an overview of the recent advances in these algorithms. This includes optimization-based methods (such as variational approximations) and simulation-based methods (such as ABC or Monte Carlo algorithms). The theoretical aspects of Approximate Bayesian Inference are covered, specifically the PAC–Bayes bounds and regret analysis. Applications for challenging computational problems in astrophysics, finance, medical data analysis, and computer vision area also presented.
Keywordsbifurcation; 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
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Publication date and placeBasel, 2022
Research & information: general
Mathematics & science