03054nam 2200685Ia 450 991078259240332120230124182730.066117333371-281-73333-497866117333391-60750-298-4600-00-0346-31-4337-1131-1(CKB)1000000000554071(EBL)334196(OCoLC)437202842(SSID)ssj0000289022(PQKBManifestationID)11221425(PQKBTitleCode)TC0000289022(PQKBWorkID)10386010(PQKB)10753325(MiAaPQ)EBC334196(Au-PeEL)EBL334196(CaPaEBR)ebr10216841(CaONFJC)MIL173333(EXLCZ)99100000000055407120071215d2008 uy 0engur|n|---|||||txtccrApproximation methods for efficient learning of Bayesian networks[electronic resource] /Carsten RiggelsenAmsterdam ;Washington, DC IOS Pressc20081 online resource (148 p.)Frontiers in artificial intelligence and applications ;v. 168Dissertations in artificial intelligenceDescription based upon print version of record.1-58603-821-4 Includes bibliographical references (p. [133]-137).Title page; Contents; Foreword; Introduction; Preliminaries; Learning Bayesian Networks from Data; Monte Carlo Methods and MCMC Simulation; Learning from Incomplete Data; Conclusion; ReferencesThis publication offers and investigates efficient Monte Carlo simulation methods in order to realize a Bayesian approach to approximate learning of Bayesian networks from both complete and incomplete data. For large amounts of incomplete data when Monte Carlo methods are inefficient, approximations are implemented, such that learning remains feasible, albeit non-Bayesian. The topics discussed are: basic concepts about probabilities, graph theory and conditional independence; Bayesian network learning from data; Monte Carlo simulation techniques; and, the concept of incomplete data. In order tFrontiers in artificial intelligence and applications.Dissertations in artificial intelligence.Frontiers in artificial intelligence and applications ;v. 168.Bayesian statistical decision theoryMachine learningNeural networks (Computer science)Bayesian statistical decision theory.Machine learning.Neural networks (Computer science)519.5519.5/42Riggelsen Carsten1560683MiAaPQMiAaPQMiAaPQBOOK9910782592403321Approximation methods for efficient learning of Bayesian networks3826835UNINA03673nam 2200745z- 450 991056646330332120220506(CKB)5680000000037748(oapen)https://directory.doabooks.org/handle/20.500.12854/80963(oapen)doab80963(EXLCZ)99568000000003774820202205d2022 |y 0engurmn|---annantxtrdacontentcrdamediacrrdacarrierCatalytic Applications of Clay Minerals and HydrotalcitesBaselMDPI - Multidisciplinary Digital Publishing Institute20221 online resource (108 p.)3-0365-3552-7 3-0365-3551-9 Clay minerals are inexpensive and available materials with a wide range of applications (adsorbent, ion exchanger, support, catalyst, paper coating, ceramic, and pharmaceutical applications, among others). Clay minerals can be easily modified through acid/basic treatments, the insertion of bulky ions or pillars into the interlayer spacing, and acid treatment, improving their physicochemical properties.Considering their low cost and high availability, clay minerals display a relatively high specific surface area in such a way that they have a great potential to be used as catalytic supports, since they can disperse expensive active phases as noble metals on the porous structures of their surfaces. In addition, the low cost of these supports allows their implementation on an industrial scale more easily than other supports, which are only feasible at the laboratory scale. Hydrotalcites (considered as anionic or basic clays) are also inexpensive materials with a great potential to be used as catalysts, since their textural properties could also be modified easily through the insertion of anions in their interlayer spacing. In the same way, these hydrotalcites, formed by layered double hydroxides, can lead to their respective mixed oxides after thermal treatment. These mixed oxides are considered basic catalysts with a high surface area, so they can also be used as catalytic support.ChemistrybicsscInorganic chemistrybicsscResearch and information: generalbicssc1,2-propanediolcoke depositionCu-based catalystsCu/ZnO/Al2O3CuMgFeesterificationexcellent durabilityfurfuralfurfuryl alcoholheterogeneous catalysthierarchical microstructurehigh selectivityhydrogenolysis of glycerolhydrotalcite-derived compositesisobutane dehydrogenationkaolinlayered double hydroxidesmeixneritemesoporousMgF2 promotern/apropane dehydrogenationPtIn/Mg(Al)O/ZnOreconstructionrecycledreduction atmospheresupported Pt-In catalystswaste valorizationChemistryInorganic chemistryResearch and information: generalCecilia Juanedt1319551Jiménez Gómez Carmen PilaredtCecilia JuanothJiménez Gómez Carmen PilarothBOOK9910566463303321Catalytic Applications of Clay Minerals and Hydrotalcites3033956UNINA