Felix okolie (6 risultati)
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Da: PBShop.store UK, Fairford, GLOS, Regno UnitoPBShop.store UK
Contatta il venditoreVenditore con 5 stelleCondizione: Nuovo
EUR 19,97
EUR 3,87 spedizioneSpedito da Regno Unito a U.S.A.Quantità: Più di 20 disponibili
PAP. Condizione: New. New Book. Shipped from UK. Established seller since 2000.
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- Print on Demand
Da: California Books, Miami, FL, U.S.A.California Books
Contatta il venditoreVenditore con 4 stelleCondizione: Nuovo
EUR 19,81
Spedizione gratuitaSpedito in U.S.A.Quantità: Più di 20 disponibili
Condizione: New. Print on Demand.
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- Print on Demand
Da: Grand Eagle Retail, Bensenville, IL, U.S.A.Grand Eagle Retail
Contatta il venditoreVenditore con 5 stelleCondizione: Nuovo
EUR 22,12
Spedizione gratuitaSpedito in U.S.A.Quantità: 1 disponibili
Paperback. Condizione: new. Paperback. Volatility modeling is central to financial econometrics, risk management, and quantitative trading. The GARCH family of models has been a cornerstone for capturing time-varying volatility, but traditional estimation approaches often underestimate uncertainty.Applied Bayesian GARCH with R p…rovides a hands-on guide to Bayesian inference for GARCH models, combining theoretical intuition with reproducible R code and case studies. You'll learn how to specify priors, run Markov chain Monte Carlo (MCMC), evaluate convergence, and forecast volatility with full uncertainty quantification.Topics covered include: Bayesian GARCH(1,1) with Gaussian and heavy-tailed errorsModel extensions: EGARCH, GJR-GARCH, and asymmetric volatilityPosterior predictive checks and model diagnosticsForecasting volatility, Value-at-Risk, and Expected ShortfallAdvanced topics: multivariate GARCH, hierarchical structures, and model averagingEach chapter contains R code, exercises, and datasets to reinforce learning. Whether you are a graduate student, researcher, or practitioner in finance, this book equips you with modern Bayesian tools to model volatility with confidence. This item is printed on demand. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.
Editore: Independently Published, 2025
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Da: Grand Eagle Retail, Bensenville, IL, U.S.A.Grand Eagle Retail
Contatta il venditoreVenditore con 5 stelleCondizione: Nuovo
EUR 26,09
Spedizione gratuitaSpedito in U.S.A.Quantità: 1 disponibili
Paperback. Condizione: new. Paperback. Understanding Machine Learning Concepts: Supervised vs. Unsupervised Learning in R is a practical, comprehensive guide that bridges theory and application for learners, researchers, and professionals in data science.Written in clear, accessible language, this book demystifies the principles… of machine learning through hands-on R implementations and real-world examples.Beginning with foundational concepts and data preprocessing, readers progress through supervised learning techniques such as regression and classification, before diving into unsupervised methods including clustering, dimensionality reduction, and association rule mining. Each chapter provides practical R code snippets, visualizations, and exercises that make complex topics intuitive and applicable.From evaluating model performance to understanding when and why to use supervised or unsupervised approaches, this book equips readers with the knowledge and confidence to build, validate, and interpret machine learning models effectively.Whether you are a student exploring data analytics, a researcher applying predictive models, or a professional seeking to expand your R programming skills, this book serves as a complete roadmap to mastering machine learning fundamentals.Highlights: A clear comparison between supervised and unsupervised learning paradigms.Step-by-step R examples for regression, classification, clustering, and dimensionality reduction.In-depth discussions on data preprocessing, feature engineering, and model validation.Real-world case studies demonstrating end-to-end R applications.A glossary of key machine learning and R terms for quick reference.Forward-looking insights into automation, interpretability, and ethical AI in R. This item is printed on demand. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.
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- Print on Demand
Da: CitiRetail, Stevenage, Regno UnitoCitiRetail
Contatta il venditoreVenditore con 5 stelleCondizione: Nuovo
EUR 23,60
EUR 43,51 spedizioneSpedito da Regno Unito a U.S.A.Quantità: 1 disponibili
Paperback. Condizione: new. Paperback. Volatility modeling is central to financial econometrics, risk management, and quantitative trading. The GARCH family of models has been a cornerstone for capturing time-varying volatility, but traditional estimation approaches often underestimate uncertainty.Applied Bayesian GARCH with R p…rovides a hands-on guide to Bayesian inference for GARCH models, combining theoretical intuition with reproducible R code and case studies. You'll learn how to specify priors, run Markov chain Monte Carlo (MCMC), evaluate convergence, and forecast volatility with full uncertainty quantification.Topics covered include: Bayesian GARCH(1,1) with Gaussian and heavy-tailed errorsModel extensions: EGARCH, GJR-GARCH, and asymmetric volatilityPosterior predictive checks and model diagnosticsForecasting volatility, Value-at-Risk, and Expected ShortfallAdvanced topics: multivariate GARCH, hierarchical structures, and model averagingEach chapter contains R code, exercises, and datasets to reinforce learning. Whether you are a graduate student, researcher, or practitioner in finance, this book equips you with modern Bayesian tools to model volatility with confidence. This item is printed on demand. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability.
Editore: Independently Published, 2025
- Brossura
- Print on Demand
Da: CitiRetail, Stevenage, Regno UnitoCitiRetail
Contatta il venditoreVenditore con 5 stelleCondizione: Nuovo
EUR 26,63
EUR 43,51 spedizioneSpedito da Regno Unito a U.S.A.Quantità: 1 disponibili
Paperback. Condizione: new. Paperback. Understanding Machine Learning Concepts: Supervised vs. Unsupervised Learning in R is a practical, comprehensive guide that bridges theory and application for learners, researchers, and professionals in data science.Written in clear, accessible language, this book demystifies the principles… of machine learning through hands-on R implementations and real-world examples.Beginning with foundational concepts and data preprocessing, readers progress through supervised learning techniques such as regression and classification, before diving into unsupervised methods including clustering, dimensionality reduction, and association rule mining. Each chapter provides practical R code snippets, visualizations, and exercises that make complex topics intuitive and applicable.From evaluating model performance to understanding when and why to use supervised or unsupervised approaches, this book equips readers with the knowledge and confidence to build, validate, and interpret machine learning models effectively.Whether you are a student exploring data analytics, a researcher applying predictive models, or a professional seeking to expand your R programming skills, this book serves as a complete roadmap to mastering machine learning fundamentals.Highlights: A clear comparison between supervised and unsupervised learning paradigms.Step-by-step R examples for regression, classification, clustering, and dimensionality reduction.In-depth discussions on data preprocessing, feature engineering, and model validation.Real-world case studies demonstrating end-to-end R applications.A glossary of key machine learning and R terms for quick reference.Forward-looking insights into automation, interpretability, and ethical AI in R. This item is printed on demand. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability.
