Bayesian inference social sciences (32 résultats)
Langue : anglais
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Langue : anglais
Edité par SAGE Publications, Inc, 1984
Série : Livre 34 sur 194 - Quantitative Applications in the Social Sciences
- Couverture souple
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Langue : anglais
Edité par SAGE Publications, Inc, 1984
Série : Livre 34 sur 194 - Quantitative Applications in the Social Sciences
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Langue : anglais
Edité par SAGE Publications, Inc, 1984
Série : Livre 34 sur 194 - Quantitative Applications in the Social Sciences
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Langue : anglais
Edité par Sage Publications, Inc, 1984
Série : Livre 34 sur 194 - Quantitative Applications in the Social Sciences
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Bayesian Inference in the Social Sciences
Yang Xin?She Yang Xin?She Jeliazkov Ivan Yang Xin-She Jeliazkov Ivan
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Hardback. Etat : New. Presents new models, methods, and techniques and considers important real-world applications in political science, sociology, economics, marketing, and finance Emphasizing interdisciplinary coverage, Bayesian Inference in the Social Sciences builds upon the recent growth in Bayesian methodology and examines… an array of topics in model formulation, estimation, and applications. The book presents recent and trending developments in a diverse, yet closely integrated, set of research topics within the social sciences and facilitates the transmission of new ideas and methodology across disciplines while maintaining manageability, coherence, and a clear focus. Bayesian Inference in the Social Sciences features innovative methodology and novel applications in addition to new theoretical developments and modeling approaches, including the formulation and analysis of models with partial observability, sample selection, and incomplete data. Additional areas of inquiry include a Bayesian derivation of empirical likelihood and method of moment estimators, and the analysis of treatment effect models with endogeneity. The book emphasizes practical implementation, reviews and extends estimation algorithms, and examines innovative applications in a multitude of fields. Time series techniques and algorithms are discussed for stochastic volatility, dynamic factor, and time-varying parameter models. Additional features include: Real-world applications and case studies that highlight asset pricing under fat-tailed distributions, price indifference modeling and market segmentation, analysis of dynamic networks, ethnic minorities and civil war, school choice effects, and business cycles and macroeconomic performanceState-of-the-art computational tools and Markov chain Monte Carlo algorithms with related materials available via the book's supplemental websiteInterdisciplinary coverage from well-known international scholars and practitioners Bayesian Inference in the Social Sciences is an ideal reference for researchers in economics, political science, sociology, and business as well as an excellent resource for academic, government, and regulation agencies. The book is also useful for graduate-level courses in applied econometrics, statistics, mathematical modeling and simulation, numerical methods, computational analysis, and the social sciences.
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Hardcover. Etat : new. Hardcover. Presents new models, methods, and techniques and considers important real-world applications in political science, sociology, economics, marketing, and finance Emphasizing interdisciplinary coverage, Bayesian Inference in the Social Sciences builds upon the recent growth in Bayesian methodology…and examines an array of topics in model formulation, estimation, and applications. The book presents recent and trending developments in a diverse, yet closely integrated, set of research topics within the social sciences and facilitates the transmission of new ideas and methodology across disciplines while maintaining manageability, coherence, and a clear focus. Bayesian Inference in the Social Sciences features innovative methodology and novel applications in addition to new theoretical developments and modeling approaches, including the formulation and analysis of models with partial observability, sample selection, and incomplete data. Additional areas of inquiry include a Bayesian derivation of empirical likelihood and method of moment estimators, and the analysis of treatment effect models with endogeneity. The book emphasizes practical implementation, reviews and extends estimation algorithms, and examines innovative applications in a multitude of fields. Time series techniques and algorithms are discussed for stochastic volatility, dynamic factor, and time-varying parameter models. Additional features include: Real-world applications and case studies that highlight asset pricing under fat-tailed distributions, price indifference modeling and market segmentation, analysis of dynamic networks, ethnic minorities and civil war, school choice effects, and business cycles and macroeconomic performanceState-of-the-art computational tools and Markov chain Monte Carlo algorithms with related materials available via the books supplemental websiteInterdisciplinary coverage from well-known international scholars and practitioners Bayesian Inference in the Social Sciences is an ideal reference for researchers in economics, political science, sociology, and business as well as an excellent resource for academic, government, and regulation agencies. The book is also useful for graduate-level courses in applied econometrics, statistics, mathematical modeling and simulation, numerical methods, computational analysis, and the social sciences. Bayesian Inference in the Social Sciences builds upon the recent growth in Bayesian methodology and examines an array of topics in model formulation, estimation, and applications. Particular emphasis is placed on an interdisciplinary coverage, model checking, and modern computational tools such as Markov chain Monte Carlo. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.
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Etat : New. Bayesian Inference in the Social Sciences builds upon the recent growth in Bayesian methodology and examines an array of topics in model formulation, estimation, and applications. Particular emphasis is placed on an interdisciplinary coverage, model checking, and modern computational tools such as Markov chain Monte…Carlo. Editor(s): Jeliazkov, Ivan; Yang, Xin-She. Num Pages: 352 pages, black & white line drawings, black & white tables, maps, figures. BIC Classification: GPS; JHBC. Category: (P) Professional & Vocational. Dimension: 243 x 161 x 23. Weight in Grams: 606. . 2014. 1st Edition. Hardcover. . . . .
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Gebunden. Etat : New. IVAN JELIAZKOV, PhD, is Associate Professor of Economics and Statistics at the University of California, Irvine. Dr. Jeliazkov s research interests include Bayesian econometrics and discrete data analysis, model comparison, and simulation-based inference. I.
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Etat : New. Bayesian Inference in the Social Sciences builds upon the recent growth in Bayesian methodology and examines an array of topics in model formulation, estimation, and applications. Particular emphasis is placed on an interdisciplinary coverage, model checking, and modern computational tools such as Markov chain Monte…Carlo. Editor(s): Jeliazkov, Ivan; Yang, Xin-She. Num Pages: 352 pages, black & white line drawings, black & white tables, maps, figures. BIC Classification: GPS; JHBC. Category: (P) Professional & Vocational. Dimension: 243 x 161 x 23. Weight in Grams: 606. . 2014. 1st Edition. Hardcover. . . . . Books ship from the US and Ireland.
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Hardback. Etat : New. Presents new models, methods, and techniques and considers important real-world applications in political science, sociology, economics, marketing, and finance Emphasizing interdisciplinary coverage, Bayesian Inference in the Social Sciences builds upon the recent growth in Bayesian methodology and examines… an array of topics in model formulation, estimation, and applications. The book presents recent and trending developments in a diverse, yet closely integrated, set of research topics within the social sciences and facilitates the transmission of new ideas and methodology across disciplines while maintaining manageability, coherence, and a clear focus. Bayesian Inference in the Social Sciences features innovative methodology and novel applications in addition to new theoretical developments and modeling approaches, including the formulation and analysis of models with partial observability, sample selection, and incomplete data. Additional areas of inquiry include a Bayesian derivation of empirical likelihood and method of moment estimators, and the analysis of treatment effect models with endogeneity. The book emphasizes practical implementation, reviews and extends estimation algorithms, and examines innovative applications in a multitude of fields. Time series techniques and algorithms are discussed for stochastic volatility, dynamic factor, and time-varying parameter models. Additional features include: Real-world applications and case studies that highlight asset pricing under fat-tailed distributions, price indifference modeling and market segmentation, analysis of dynamic networks, ethnic minorities and civil war, school choice effects, and business cycles and macroeconomic performanceState-of-the-art computational tools and Markov chain Monte Carlo algorithms with related materials available via the book's supplemental websiteInterdisciplinary coverage from well-known international scholars and practitioners Bayesian Inference in the Social Sciences is an ideal reference for researchers in economics, political science, sociology, and business as well as an excellent resource for academic, government, and regulation agencies. The book is also useful for graduate-level courses in applied econometrics, statistics, mathematical modeling and simulation, numerical methods, computational analysis, and the social sciences.
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Hardcover. Etat : new. Hardcover. Presents new models, methods, and techniques and considers important real-world applications in political science, sociology, economics, marketing, and finance Emphasizing interdisciplinary coverage, Bayesian Inference in the Social Sciences builds upon the recent growth in Bayesian methodology…and examines an array of topics in model formulation, estimation, and applications. The book presents recent and trending developments in a diverse, yet closely integrated, set of research topics within the social sciences and facilitates the transmission of new ideas and methodology across disciplines while maintaining manageability, coherence, and a clear focus. Bayesian Inference in the Social Sciences features innovative methodology and novel applications in addition to new theoretical developments and modeling approaches, including the formulation and analysis of models with partial observability, sample selection, and incomplete data. Additional areas of inquiry include a Bayesian derivation of empirical likelihood and method of moment estimators, and the analysis of treatment effect models with endogeneity. The book emphasizes practical implementation, reviews and extends estimation algorithms, and examines innovative applications in a multitude of fields. Time series techniques and algorithms are discussed for stochastic volatility, dynamic factor, and time-varying parameter models. Additional features include: Real-world applications and case studies that highlight asset pricing under fat-tailed distributions, price indifference modeling and market segmentation, analysis of dynamic networks, ethnic minorities and civil war, school choice effects, and business cycles and macroeconomic performanceState-of-the-art computational tools and Markov chain Monte Carlo algorithms with related materials available via the books supplemental websiteInterdisciplinary coverage from well-known international scholars and practitioners Bayesian Inference in the Social Sciences is an ideal reference for researchers in economics, political science, sociology, and business as well as an excellent resource for academic, government, and regulation agencies. The book is also useful for graduate-level courses in applied econometrics, statistics, mathematical modeling and simulation, numerical methods, computational analysis, and the social sciences. Bayesian Inference in the Social Sciences builds upon the recent growth in Bayesian methodology and examines an array of topics in model formulation, estimation, and applications. Particular emphasis is placed on an interdisciplinary coverage, model checking, and modern computational tools such as Markov chain Monte Carlo. Shipping may be from our Sydney, NSW warehouse or from our UK or US warehouse, depending on stock availability.
