{"product_id":"bayesian-inference-and-mcmc-methods-alice-schwartz-9798180568571","title":"Bayesian Inference and MCMC Methods for Finance: Hierarchical Models, Portfolio Optimization, and Uncertainty Quantification","description":"\u003cb\u003eReactive Publishing\u003c\/b\u003e\u003cp\u003eBayesian Inference and MCMC Methods for Finance provides a rigorous, practical introduction to modern Bayesian techniques and Markov Chain Monte Carlo (MCMC) methods tailored specifically for financial applications.\u003c\/p\u003e\u003cp\u003eThis book bridges the gap between theoretical statistics and real-world quantitative finance by demonstrating how hierarchical Bayesian models, advanced MCMC sampling, and uncertainty quantification can be applied to portfolio optimization, risk management, and decision-making under uncertainty. Readers will explore the construction and implementation of hierarchical models for capturing complex dependencies in financial data, along with robust methods for posterior inference and predictive simulation.\u003c\/p\u003e\u003cp\u003eKey topics include: \u003c\/p\u003e\u003cul\u003e\n\u003cli\u003eFundamentals of Bayesian inference and its advantages over classical frequentist approaches in finance\u003c\/li\u003e\n\u003cli\u003ePractical MCMC algorithms, including Metropolis-Hastings, Gibbs sampling, and Hamiltonian Monte Carlo\u003c\/li\u003e\n\u003cli\u003eHierarchical modeling techniques for multi-level financial data\u003c\/li\u003e\n\u003cli\u003eBayesian approaches to portfolio optimization and asset allocation\u003c\/li\u003e\n\u003cli\u003eUncertainty quantification in risk assessment and forecasting\u003c\/li\u003e\n\u003cli\u003eImplementation strategies using Python and relevant probabilistic programming libraries\u003c\/li\u003e\n\u003c\/ul\u003e\u003cp\u003eWritten for quantitative analysts, portfolio managers, researchers, and graduate students in finance and statistics, this book emphasizes clarity, mathematical precision, and reproducible computational methods. It equips practitioners with the tools needed to move beyond point estimates and incorporate probabilistic thinking into financial modeling workflows.\u003c\/p\u003e\u003cp\u003eWhether you are looking to enhance existing models with Bayesian robustness or build new systems that properly account for parameter and model uncertainty, this volume offers a focused, technical foundation for applying Bayesian methods in finance.\u003c\/p\u003e\u003cbr\u003e\u003cbr\u003e\u003cb\u003eAuthor:\u003c\/b\u003e Alice Schwartz,Vincent Bisette\u003cbr\u003e\u003cb\u003eISBN-13:\u003c\/b\u003e 9798180568571\u003cbr\u003e\u003cb\u003ePublisher:\u003c\/b\u003e Independently Published\u003cbr\u003e\u003cb\u003eLanguage:\u003c\/b\u003e English\u003cbr\u003e\u003cb\u003ePublished:\u003c\/b\u003e 06\/07\/2026\u003cbr\u003e\u003cb\u003ePages:\u003c\/b\u003e 402\u003cbr\u003e\u003cb\u003eFormat:\u003c\/b\u003e Paperback\u003cbr\u003e\u003cb\u003eWeight:\u003c\/b\u003e 1.07lbs\u003cbr\u003e\u003cb\u003eSize:\u003c\/b\u003e 9.00h x 6.00w x 1.00d","brand":"Alice Schwartz","offers":[{"title":"Paperback","offer_id":49001985704191,"sku":"9798180568571","price":33.99,"currency_code":"USD","in_stock":true}],"url":"https:\/\/www.whiterainbookhouse.com\/products\/bayesian-inference-and-mcmc-methods-alice-schwartz-9798180568571","provider":"WR Book House","version":"1.0","type":"link"}