LEADER 05340nam 2201309z- 450 001 9910576874903321 005 20231214132829.0 035 $a(CKB)5720000000008426 035 $a(oapen)https://directory.doabooks.org/handle/20.500.12854/84560 035 $a(EXLCZ)995720000000008426 100 $a20202206d2022 |y 0 101 0 $aeng 135 $aurmn|---annan 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aApproximate Bayesian Inference 210 $aBasel$cMDPI - Multidisciplinary Digital Publishing Institute$d2022 215 $a1 electronic resource (508 p.) 311 $a3-0365-3789-9 311 $a3-0365-3790-2 330 $aExtremely 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. 606 $aResearch & information: general$2bicssc 606 $aMathematics & science$2bicssc 610 $abifurcation 610 $adynamical systems 610 $aEdward?Sokal coupling 610 $amean-field 610 $aKullback?Leibler divergence 610 $avariational inference 610 $aBayesian statistics 610 $amachine learning 610 $avariational approximations 610 $aPAC-Bayes 610 $aexpectation-propagation 610 $aMarkov chain Monte Carlo 610 $aLangevin Monte Carlo 610 $asequential Monte Carlo 610 $aLaplace approximations 610 $aapproximate Bayesian computation 610 $aGibbs posterior 610 $aMCMC 610 $astochastic gradients 610 $aneural networks 610 $aApproximate Bayesian Computation 610 $adifferential evolution 610 $aMarkov kernels 610 $adiscrete state space 610 $aergodicity 610 $aMarkov chain 610 $aprobably approximately correct 610 $avariational Bayes 610 $aBayesian inference 610 $aMarkov Chain Monte Carlo 610 $aSequential Monte Carlo 610 $aRiemann Manifold Hamiltonian Monte Carlo 610 $aintegrated nested laplace approximation 610 $afixed-form variational Bayes 610 $astochastic volatility 610 $anetwork modeling 610 $anetwork variability 610 $aStiefel manifold 610 $aMCMC-SAEM 610 $adata imputation 610 $aBethe free energy 610 $afactor graphs 610 $amessage passing 610 $avariational free energy 610 $avariational message passing 610 $aapproximate Bayesian computation (ABC) 610 $adifferential privacy (DP) 610 $asparse vector technique (SVT) 610 $aGaussian 610 $aparticle flow 610 $avariable flow 610 $aLangevin dynamics 610 $aHamilton Monte Carlo 610 $anon-reversible dynamics 610 $acontrol variates 610 $athinning 610 $ameta-learning 610 $ahyperparameters 610 $apriors 610 $aonline learning 610 $aonline optimization 610 $agradient descent 610 $astatistical learning theory 610 $aPAC?Bayes theory 610 $adeep learning 610 $ageneralisation bounds 610 $aBayesian sampling 610 $aMonte Carlo integration 610 $aPAC-Bayes theory 610 $ano free lunch theorems 610 $asequential learning 610 $aprincipal curves 610 $adata streams 610 $aregret bounds 610 $agreedy algorithm 610 $asleeping experts 610 $aentropy 610 $arobustness 610 $astatistical mechanics 610 $acomplex systems 615 7$aResearch & information: general 615 7$aMathematics & science 700 $aAlquier$b Pierre$4edt$01307630 702 $aAlquier$b Pierre$4oth 906 $aBOOK 912 $a9910576874903321 996 $aApproximate Bayesian Inference$93028878 997 $aUNINA