| Titre : |
Bayesian Inference for Stochastic Processes |
| Type de document : |
texte imprimé |
| Auteurs : |
Michael Davim, Auteur |
| Editeur : |
TSP Publishing |
| Année de publication : |
2026 |
| Importance : |
292 p. |
| Présentation : |
ill. couv. en noir |
| Format : |
25x17.5 cm |
| ISBN/ISSN/EAN : |
978-99691-23968-- |
| Prix : |
29 300,00 DA |
| Note générale : |
Publisher : TSP Publishing (Algérie)
Publication date : 2026
Language : English
Print length : 292 pages
ISBN-10 : -
ISBN-13 : 978-99691-23968
Item weight : 708 g |
| Langues : |
Anglais (eng) |
| Mots-clés : |
Stochastic Processes baysian bayesian inference bayesian netwoek hypothesis testing |
| Index. décimale : |
519 |
| Résumé : |
Bayesian Inference for Stochastic Processes
Bayesian inference is developed based on the simple Bayesian rule in probability theory, but this method of thinking is one of the most important findings in the history of science. Bayesian inference has found application in a wide range of activities, including philosophy, medicine, sport, and law.
In the philosophy of decision theory, Bayesian inference is closely related to subjective probability, often called “Bayesian probability”. It means that we can modify our beliefs about nature by gathering data and analyzing the behavior of people around us or by investigating interesting historical events.
Bayesian inference can assign probabilities to each random process. In the Bayesian sense, probability is a way to show an individual's degree of belief in a statement. Bayesian inferences have different interpretations of probability, and also different approaches depend on those interpretations. Bayes' theorem presents the relationship between two conditional probabilities that are the reverse of each other.
This book takes a look at both the theoretical foundations of Bayesian inference and practical implementations in stochastic processes. The book uses the Bayesian approach to make statistical inferences about stochastic processes and offers a practical approach by considering real-world practices of interest to the scientific community. This book will serve as a reference guide for the application of Bayesian inference in the field of stochastic processes, as well as a source document of fundamentals for intermediate Bayesian readers.
Michael Davim, PhD, is currently working as an associate professor at the Department of Mechanical Engineering. He has several scientific publications, including international conference papers, journal papers, and book chapters. He has been a reviewer of well-known robotic journals. His research interests are optimization, control, kinematics, and dynamics modeling of serial and parallel robotic manipulators.
|
| Note de contenu : |
index |
Bayesian Inference for Stochastic Processes [texte imprimé] / Michael Davim, Auteur . - TSP Publishing, 2026 . - 292 p. : ill. couv. en noir ; 25x17.5 cm. ISBN : 978-99691-23968-- : 29 300,00 DA Publisher : TSP Publishing (Algérie)
Publication date : 2026
Language : English
Print length : 292 pages
ISBN-10 : -
ISBN-13 : 978-99691-23968
Item weight : 708 g Langues : Anglais ( eng)
| Mots-clés : |
Stochastic Processes baysian bayesian inference bayesian netwoek hypothesis testing |
| Index. décimale : |
519 |
| Résumé : |
Bayesian Inference for Stochastic Processes
Bayesian inference is developed based on the simple Bayesian rule in probability theory, but this method of thinking is one of the most important findings in the history of science. Bayesian inference has found application in a wide range of activities, including philosophy, medicine, sport, and law.
In the philosophy of decision theory, Bayesian inference is closely related to subjective probability, often called “Bayesian probability”. It means that we can modify our beliefs about nature by gathering data and analyzing the behavior of people around us or by investigating interesting historical events.
Bayesian inference can assign probabilities to each random process. In the Bayesian sense, probability is a way to show an individual's degree of belief in a statement. Bayesian inferences have different interpretations of probability, and also different approaches depend on those interpretations. Bayes' theorem presents the relationship between two conditional probabilities that are the reverse of each other.
This book takes a look at both the theoretical foundations of Bayesian inference and practical implementations in stochastic processes. The book uses the Bayesian approach to make statistical inferences about stochastic processes and offers a practical approach by considering real-world practices of interest to the scientific community. This book will serve as a reference guide for the application of Bayesian inference in the field of stochastic processes, as well as a source document of fundamentals for intermediate Bayesian readers.
Michael Davim, PhD, is currently working as an associate professor at the Department of Mechanical Engineering. He has several scientific publications, including international conference papers, journal papers, and book chapters. He has been a reviewer of well-known robotic journals. His research interests are optimization, control, kinematics, and dynamics modeling of serial and parallel robotic manipulators.
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| Note de contenu : |
index |
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