LEADER 02145nam 2200601 450 001 9910455698103321 005 20200520144314.0 010 $a0-309-37034-5 010 $a0-585-02170-8 035 $a(CKB)110986584752172 035 $a(SSID)ssj0000261441 035 $a(PQKBManifestationID)12079588 035 $a(PQKBTitleCode)TC0000261441 035 $a(PQKBWorkID)10256707 035 $a(PQKB)10283187 035 $a(MiAaPQ)EBC3439828 035 $a(Au-PeEL)EBL3439828 035 $a(CaPaEBR)ebr11091737 035 $a(OCoLC)811251476 035 $a(EXLCZ)99110986584752172 100 $a20150907h19971997 uy 0 101 0 $aeng 135 $aurcnu|||||||| 181 $ctxt 182 $cc 183 $acr 200 00$aTransforming post-Communist political economies /$fJoan M. Nelson, Charles Tilly, and Lee Walker, editors ; Task Force on Economies in Transition, Commission on Behavioral and Social Sciences and Education, National Research Council 210 1$aWashington, District of Columbia :$cNational Academy Press,$d1997. 210 4$d©1997 215 $a1 online resource (523 pages) 300 $aBibliographic Level Mode of Issuance: Monograph 311 $a0-309-05929-1 320 $aIncludes bibliographical references and index. 606 $aPost-communism$zFormer Soviet republics 606 $aSocial change$zFormer Soviet republics 607 $aFormer Soviet republics$xEconomic conditions 607 $aFormer Soviet republics$xEconomic policy 607 $aFormer Soviet republics$xSocial conditions 608 $aElectronic books. 615 0$aPost-communism 615 0$aSocial change 676 $a338.947 702 $aNelson$b Joan M. 702 $aTilly$b Charles 702 $aWalker$b Lee 712 02$aNational Research Council (U.S.).$bCommission on Behavioral and Social Sciences and Education.$bTask Force on Economies in Transition. 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910455698103321 996 $aTransforming post-communist political economies$91261278 997 $aUNINA LEADER 04838nam 22005775 450 001 996418260403316 005 20210608195711.0 010 $a3-030-41068-4 024 7 $a10.1007/978-3-030-41068-1 035 $a(CKB)4100000011325693 035 $a(DE-He213)978-3-030-41068-1 035 $a(MiAaPQ)EBC6247297 035 $a(PPN)266776698 035 $a(EXLCZ)994100000011325693 100 $a20200701d2020 u| 0 101 0 $aeng 135 $aurnn#008mamaa 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aMachine Learning in Finance$b[electronic resource] $eFrom Theory to Practice /$fby Matthew F. Dixon, Igor Halperin, Paul Bilokon 205 $a1st ed. 2020. 210 1$aCham :$cSpringer International Publishing :$cImprint: Springer,$d2020. 215 $a1 online resource (XXV, 548 p. 97 illus., 83 illus. in color.) 311 $a3-030-41067-6 320 $aIncludes bibliographical references and index. 327 $aChapter 1. Introduction -- Chapter 2. Probabilistic Modeling -- Chapter 3. Bayesian Regression & Gaussian Processes -- Chapter 4. Feed Forward Neural Networks -- Chapter 5. Interpretability -- Chapter 6. Sequence Modeling -- Chapter 7. Probabilistic Sequence Modeling -- Chapter 8. Advanced Neural Networks -- Chapter 9. Introduction to Reinforcement learning -- Chapter 10. Applications of Reinforcement Learning -- Chapter 11. Inverse Reinforcement Learning and Imitation Learning -- Chapter 12. Frontiers of Machine Learning and Finance. 330 $aThis book introduces machine learning methods in finance. It presents a unified treatment of machine learning and various statistical and computational disciplines in quantitative finance, such as financial econometrics and discrete time stochastic control, with an emphasis on how theory and hypothesis tests inform the choice of algorithm for financial data modeling and decision making. With the trend towards increasing computational resources and larger datasets, machine learning has grown into an important skillset for the finance industry. This book is written for advanced graduate students and academics in financial econometrics, mathematical finance and applied statistics, in addition to quants and data scientists in the field of quantitative finance. Machine Learning in Finance: From Theory to Practice is divided into three parts, each part covering theory and applications. The first presents supervised learning for cross-sectional data from both a Bayesian and frequentist perspective. The more advanced material places a firm emphasis on neural networks, including deep learning, as well as Gaussian processes, with examples in investment management and derivative modeling. The second part presents supervised learning for time series data, arguably the most common data type used in finance with examples in trading, stochastic volatility and fixed income modeling. Finally, the third part presents reinforcement learning and its applications in trading, investment and wealth management. Python code examples are provided to support the readers' understanding of the methodologies and applications. The book also includes more than 80 mathematical and programming exercises, with worked solutions available to instructors. As a bridge to research in this emergent field, the final chapter presents the frontiers of machine learning in finance from a researcher's perspective, highlighting how many well-known concepts in statistical physics are likely to emerge as important methodologies for machine learning in finance. 606 $aStatistics  606 $aApplied mathematics 606 $aEngineering mathematics 606 $aStatistics for Business, Management, Economics, Finance, Insurance$3https://scigraph.springernature.com/ontologies/product-market-codes/S17010 606 $aApplications of Mathematics$3https://scigraph.springernature.com/ontologies/product-market-codes/M13003 606 $aStatistics, general$3https://scigraph.springernature.com/ontologies/product-market-codes/S0000X 615 0$aStatistics . 615 0$aApplied mathematics. 615 0$aEngineering mathematics. 615 14$aStatistics for Business, Management, Economics, Finance, Insurance. 615 24$aApplications of Mathematics. 615 24$aStatistics, general. 676 $a332.0285554 700 $aDixon$b Matthew F$4aut$4http://id.loc.gov/vocabulary/relators/aut$01002777 702 $aHalperin$b Igor$4aut$4http://id.loc.gov/vocabulary/relators/aut 702 $aBilokon$b Paul$4aut$4http://id.loc.gov/vocabulary/relators/aut 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a996418260403316 996 $aMachine Learning in Finance$92301701 997 $aUNISA