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Random Processes & Markov Chains: A Comprehensive Guide to Stochastic Modeling and Applications - Couverture souple
Synopsis
Reactive Publishing
Master Random Processes and Markov Chains for Real-World Applications
Random processes and Markov chains form the foundation of stochastic modeling, widely used in fields like finance, engineering, machine learning, and operations research. These mathematical tools help model uncertainty, decision-making, and dynamic systems, providing insights into everything from financial markets and queuing systems to AI algorithms and biological processes.
This comprehensive guide breaks down complex topics into clear explanations and practical applications, making it ideal for students, researchers, and professionals who want to build a strong mathematical foundation in stochastic processes.
What You’ll Learn:Fundamentals of Random Processes – Poisson processes, Gaussian processes, and Wiener processes
Discrete-Time & Continuous-Time Markov Chains – Transition probabilities, steady-state analysis, and Chapman-Kolmogorov equations
Stochastic Modeling Techniques – Applications in queuing theory, inventory management, and dynamic systems
Hidden Markov Models (HMMs) – Applications in speech recognition, finance, and artificial intelligence
Martingales & Stochastic Optimization – How probability models are used in decision-making under uncertainty
Monte Carlo Simulations & Markov Chain Monte Carlo (MCMC) – Numerical methods for complex stochastic systems
Practical Examples & Case Studies – Applications in economics, physics, engineering, and data science
Students & Researchers – Build a solid foundation in probability, stochastic processes, and Markov models
Engineers & Scientists – Apply stochastic modeling techniques to real-world problems
Data Scientists & AI Practitioners – Leverage Markov chains for machine learning and predictive analytics
Finance & Business Professionals – Use Markov models for risk analysis and market prediction
With clear explanations, real-world applications, and step-by-step examples, this book makes random processes and Markov chains accessible to a broad audience.
Master stochastic modeling—get your copy today!
Les informations fournies dans la section « Synopsis » peuvent faire référence à une autre édition de ce titre.
- ÉditeurIndependently published
- Date d'édition2025
- ISBN 13 9798312327731
- ReliureBroché
- Langueanglais
- Nombre de pages415
- Coordonnées du fabricantnon disponible
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Random Processes & Markov Chains: A Comprehensive Guide to Stochastic Modeling and Applications
Vendeur : Ria Christie Collections, Uxbridge, Royaume-Uni
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Random Processes & Markov Chains: A Comprehensive Guide to Stochastic Modeling and Applications
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Random Processes & Markov Chains (Paperback)
Vendeur : CitiRetail, Stevenage, Royaume-Uni
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Paperback. Etat : new. Paperback. Reactive PublishingMaster Random Processes and Markov Chains for Real-World ApplicationsRandom processes and Markov chains form the foundation of stochastic modeling, widely used in fields like finance, engineering, machine learning, and operations research. These mathematical tools help model uncertainty, decision-making, and dynamic systems, providing insights into everything from financial markets and queuing systems to AI algorithms and biological processes.This comprehensive guide breaks down complex topics into clear explanations and practical applications, making it ideal for students, researchers, and professionals who want to build a strong mathematical foundation in stochastic processes.What You'll Learn: Fundamentals of Random Processes - Poisson processes, Gaussian processes, and Wiener processesDiscrete-Time & Continuous-Time Markov Chains - Transition probabilities, steady-state analysis, and Chapman-Kolmogorov equationsStochastic Modeling Techniques - Applications in queuing theory, inventory management, and dynamic systemsHidden Markov Models (HMMs) - Applications in speech recognition, finance, and artificial intelligenceMartingales & Stochastic Optimization - How probability models are used in decision-making under uncertaintyMonte Carlo Simulations & Markov Chain Monte Carlo (MCMC) - Numerical methods for complex stochastic systemsPractical Examples & Case Studies - Applications in economics, physics, engineering, and data scienceWho This Book is For: Students & Researchers - Build a solid foundation in probability, stochastic processes, and Markov models Engineers & Scientists - Apply stochastic modeling techniques to real-world problemsData Scientists & AI Practitioners - Leverage Markov chains for machine learning and predictive analyticsFinance & Business Professionals - Use Markov models for risk analysis and market predictionWith clear explanations, real-world applications, and step-by-step examples, this book makes random processes and Markov chains accessible to a broad audience.Master stochastic modeling-get your copy today! Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability. N° de réf. du vendeur 9798312327731
Quantité disponible : 1 disponible(s)
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Random Processes & Markov Chains (Paperback)
Vendeur : Grand Eagle Retail, Fairfield, OH, Etats-Unis
Évaluation du vendeur 5 sur 5 étoiles
Paperback. Etat : new. Paperback. Reactive PublishingMaster Random Processes and Markov Chains for Real-World ApplicationsRandom processes and Markov chains form the foundation of stochastic modeling, widely used in fields like finance, engineering, machine learning, and operations research. These mathematical tools help model uncertainty, decision-making, and dynamic systems, providing insights into everything from financial markets and queuing systems to AI algorithms and biological processes.This comprehensive guide breaks down complex topics into clear explanations and practical applications, making it ideal for students, researchers, and professionals who want to build a strong mathematical foundation in stochastic processes.What You'll Learn: Fundamentals of Random Processes - Poisson processes, Gaussian processes, and Wiener processesDiscrete-Time & Continuous-Time Markov Chains - Transition probabilities, steady-state analysis, and Chapman-Kolmogorov equationsStochastic Modeling Techniques - Applications in queuing theory, inventory management, and dynamic systemsHidden Markov Models (HMMs) - Applications in speech recognition, finance, and artificial intelligenceMartingales & Stochastic Optimization - How probability models are used in decision-making under uncertaintyMonte Carlo Simulations & Markov Chain Monte Carlo (MCMC) - Numerical methods for complex stochastic systemsPractical Examples & Case Studies - Applications in economics, physics, engineering, and data scienceWho This Book is For: Students & Researchers - Build a solid foundation in probability, stochastic processes, and Markov models Engineers & Scientists - Apply stochastic modeling techniques to real-world problemsData Scientists & AI Practitioners - Leverage Markov chains for machine learning and predictive analyticsFinance & Business Professionals - Use Markov models for risk analysis and market predictionWith clear explanations, real-world applications, and step-by-step examples, this book makes random processes and Markov chains accessible to a broad audience.Master stochastic modeling-get your copy today! Shipping may be from multiple locations in the US or from the UK, depending on stock availability. N° de réf. du vendeur 9798312327731
Quantité disponible : 1 disponible(s)