LEADER 01404nam0-2200361 --450 001 9910317359303321 005 20190430155830.0 010 $a978-2-503-54949-1 100 $a20190430d2013----kmuy0itay5050 ba 101 0 $ager$aita 102 $aPT 105 $ay 011yy 200 1 $aPhronêsis - Prudentia - Klugheit$edas Wissen des Klugen im Mittelalter, Renaissance und Neuzeit$dIl sapere del saggio nel Medioevo, nel Rinascimento e nell'età moderna$eMatthias Lutz-Bachmann zu seinem 60. Geburtstag$fherausgegeben von Alexander Fidora, Andreas Niederberger, Merio Scattola 210 $aPorto$cFédération Internationale des Instituts d'Études Médiévales$d2013 215 $a348 p.$d24 cm 225 1 $aTextes et études du Moyen âge$v68 510 1 $a<>sapere del saggio nel Medioevo, nel Rinascimento e nell'età moderna 610 0 $aSaggezza 610 0 $aFilosofia antica$aMedioevo$aSaggi 610 0 $aFilosofia antica$aRinascimento$aSaggi 676 $a189$v22$zita 676 $a190$v22$zita 702 1$aLutz-Bachmann,$bMatthias 702 1$aNiederberger,$bAndreas 702 1$aScattola,$bMerio 702 1$aFidora,$bAlexander 801 0$aIT$bUNINA$gREICAT$2UNIMARC 901 $aBK 912 $a9910317359303321 952 $aP.1 M/FG 277$bbibl. 2019$fFLFBC 959 $aFLFBC 996 $aPhronêsis - Prudentia - Klugheit$91550772 997 $aUNINA LEADER 05355nam 2201321z- 450 001 9910576874903321 005 20220621 035 $a(CKB)5720000000008426 035 $a(oapen)https://directory.doabooks.org/handle/20.500.12854/84560 035 $a(oapen)doab84560 035 $a(EXLCZ)995720000000008426 100 $a20202206d2022 |y 0 101 0 $aeng 135 $aurmn|---annan 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 00$aApproximate Bayesian Inference 210 $aBasel$cMDPI - Multidisciplinary Digital Publishing Institute$d2022 215 $a1 online resource (508 p.) 311 08$a3-0365-3789-9 311 08$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 $aMathematics and Science$2bicssc 606 $aResearch and information: general$2bicssc 610 $aapproximate Bayesian computation 610 $aApproximate Bayesian Computation 610 $aapproximate Bayesian computation (ABC) 610 $aBayesian inference 610 $aBayesian sampling 610 $aBayesian statistics 610 $aBethe free energy 610 $abifurcation 610 $acomplex systems 610 $acontrol variates 610 $adata imputation 610 $adata streams 610 $adeep learning 610 $adifferential evolution 610 $adifferential privacy (DP) 610 $adiscrete state space 610 $adynamical systems 610 $aEdward-Sokal coupling 610 $aentropy 610 $aergodicity 610 $aexpectation-propagation 610 $afactor graphs 610 $afixed-form variational Bayes 610 $aGaussian 610 $ageneralisation bounds 610 $aGibbs posterior 610 $agradient descent 610 $agreedy algorithm 610 $aHamilton Monte Carlo 610 $ahyperparameters 610 $aintegrated nested laplace approximation 610 $aKullback-Leibler divergence 610 $aLangevin dynamics 610 $aLangevin Monte Carlo 610 $aLaplace approximations 610 $amachine learning 610 $aMarkov chain 610 $aMarkov chain Monte Carlo 610 $aMarkov Chain Monte Carlo 610 $aMarkov kernels 610 $aMCMC 610 $aMCMC-SAEM 610 $amean-field 610 $amessage passing 610 $ameta-learning 610 $aMonte Carlo integration 610 $anetwork modeling 610 $anetwork variability 610 $aneural networks 610 $ano free lunch theorems 610 $anon-reversible dynamics 610 $aonline learning 610 $aonline optimization 610 $aPAC-Bayes 610 $aPAC-Bayes theory 610 $aPAC-Bayes theory 610 $aparticle flow 610 $aprincipal curves 610 $apriors 610 $aprobably approximately correct 610 $aregret bounds 610 $aRiemann Manifold Hamiltonian Monte Carlo 610 $arobustness 610 $asequential learning 610 $asequential Monte Carlo 610 $aSequential Monte Carlo 610 $asleeping experts 610 $asparse vector technique (SVT) 610 $astatistical learning theory 610 $astatistical mechanics 610 $aStiefel manifold 610 $astochastic gradients 610 $astochastic volatility 610 $athinning 610 $avariable flow 610 $avariational approximations 610 $avariational Bayes 610 $avariational free energy 610 $avariational inference 610 $avariational message passing 615 7$aMathematics and Science 615 7$aResearch and information: general 700 $aAlquier$b Pierre$4edt$01307630 702 $aAlquier$b Pierre$4oth 906 $aBOOK 912 $a9910576874903321 996 $aApproximate Bayesian Inference$93028878 997 $aUNINA