Sujit Sahu's Books

Bayesian Modeling of Spatio-Temporal Data with R

By: Sujit Sahu

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Bayesian Modeling of Spatio-Temporal Data with R

By: Sujit Sahu

Applied sciences, both physical and social, such as atmospheric, biological, climate, demographic, economic, ecological, environmental, oceanic and political, routinely gather large volumes of spatial and spatio-temporal data in order to make wide ranging inference and prediction. Ideally such inferential tasks should be approached through modelling, which aids in estimation of uncertainties in all conclusions drawn from such data. Unified Bayesian modelling, implemented through user friendly software packages, provides a crucial key to unlocking the full power of these methods for solving challenging practical problems. Key features of the book: • Accessible detailed discussion of a majority of all aspects of Bayesian methods and computations with worked examples, numerical illustrations and exercises • A spatial statistics jargon buster chapter that enables the reader to build up a vocabulary without getting clouded in modeling and technicalities • Computation and modeling illustrations are provided with the help of the dedicated R package bmstdr, allowing the reader to use well-known packages and platforms, such as rstan, INLA, spBayes, spTimer, spTDyn, CARBayes, CARBayesST, etc • Included are R code notes detailing the algorithms used to produce all the tables and figures, with data and code available via an online supplement • Two dedicated chapters discuss practical examples of spatio-temporal modeling of point referenced and areal unit data • Throughout, the emphasis has been on validating models by splitting data into test and training sets following on the philosophy of machine learning and data science This book is designed to make spatio-temporal modeling and analysis accessible and understandable to a wide audience of students and researchers, from mathematicians and statisticians to practitioners in the applied sciences. It presents most of the modeling with the help of R commands written in a purposefully developed R package to facilitate spatio-temporal modeling. It does not compromise on rigour, as it presents the underlying theories of Bayesian inference and computation in standalone chapters, which would be appeal those interested in the theoretical details. By avoiding hard core mathematics and calculus, this book aims to be a bridge that removes the statistical knowledge gap from among the applied scientists.

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Page Count

385

Publisher

CRC Press

Published Date

ISBN 10

1000543692

ISBN 13

9781000543698

Introduction to Probability, Statistics and R

Foundations for Data-based Sciences

By: Sujit K. Sahu

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Introduction to Probability, Statistics and R

Foundations for Data-based Sciences

By: Sujit K. Sahu

Zusammenfassung: A strong grasp of elementary statistics and probability, along with basic skills in using R, is essential for various scientific disciplines reliant on data analysis. This book serves as a gateway to learning statistical methods from scratch, assuming a solid background in high school mathematics. Readers gradually progress from basic concepts to advanced statistical modelling, with examples from actuarial, biological, ecological, engineering, environmental, medicine, and social sciences highlighting the real-world relevance of the subject. An accompanying R package enables seamless practice and immediate application, making it ideal for beginners. The book comprises 19 chapters divided into five parts. Part I introduces basic statistics and the R software package, teaching readers to calculate simple statistics and create basic data graphs. Part II delves into probability concepts, including rules and conditional probability, and introduces widely used discrete and continuous probability distributions (e.g., binomial, Poisson, normal, log-normal). It concludes with the central limit theorem and joint distributions for multiple random variables. Part III explores statistical inference, covering point and interval estimation, hypothesis testing, and Bayesian inference. This part is intentionally less technical, making it accessible to readers without an extensive mathematical background. Part IV addresses advanced probability and statistical distribution theory, assuming some familiarity with (or concurrent study of) mathematical methods like advanced calculus and linear algebra. Finally, Part V focuses on advanced statistical modelling using simple and multiple regression and analysis of variance, laying the foundation for further studies in machine learning and data science applicable to various data and decision analytics contexts. Based on years of teaching experience, this textbook includes numerous exercises and makes extensive use of R, making it ideal for year-long data science modules and courses. In addition to university courses, the book amply covers the syllabus for the Actuarial Statistics 1 examination of the Institute and Faculty of Actuaries in London. It also provides a solid foundation for postgraduate studies in statistics and probability, or a reliable reference for statistics

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Page Count

560

Publisher

Springer Nature

Published Date

ISBN 10

3031378652

ISBN 13

9783031378652

Pharmaceutical Chemistry- Theory

Pharmaceutical Chemistry- FIRST YEAR DIPLOMA IN PHARMACY (AS PER PCI ER-2020)

By: Prof. (Dr.) Suman Acharyya , Dr. H. K. Sundeep Kumar , Miss Ritika Pradhan , Dr. Pratap Kumar Patra , Dr. Sujit Kumar Sahu

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Pharmaceutical Chemistry- Theory

Pharmaceutical Chemistry- FIRST YEAR DIPLOMA IN PHARMACY (AS PER PCI ER-2020)

By: Prof. (Dr.) Suman Acharyya , Dr. H. K. Sundeep Kumar , Miss Ritika Pradhan , Dr. Pratap Kumar Patra , Dr. Sujit Kumar Sahu

It deals with the study of inorganic drugs based on pharmacological classification. It also lays emphasis on the chemistry as a knowledge of the chemical properties, which will help the reader in understanding the rationale behind the tests for identity and also the storage conditions. The book is student-friendly as it is written in an understandable way, covering the entire syllabus of D.Pharm prescribed by Pharmacy Council of India (PCI) ER 2020. The matter is presented in such a way as to avoid confusion and to make the reading of the book a pleasurable experience. The lucid language of the book would facilitate quick revision.

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Page Count

435

Publisher

Shashwat Publication

Published Date

ISBN 10

9360878529

ISBN 13

9789360878528

The Confession of Sultana Daku

By: Sujit Saraf

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The Confession of Sultana Daku

By: Sujit Saraf

7 July 1924. Sultana Daku, notorious leader of a gang of bhantu dacoits that terrorized the towns and villages of the United Provinces, awaits Lt. Col. Samuel Pearce’s arrival in Haldwani jail. It is Sultana’s last night. In the morning he will be hanged. Wrapped in a haze of charas and nostalgia, the daku speaks all night as the Englishman listens. He recounts tales of incredible feats and narrow escapes, of the camaraderie he shared with his bhantu companions, of his love for the nautanki dancer Phulkanwar, and of the shocking betrayal that brought him to the gallows. But even as Pearce and the reader are drawn into Sultana’s confession, the contradictions that emerge reveal the daku’s own demons—his fears, superstitions and ruthless excesses—and an unshakeable belief in his criminal destiny that clashes all too often with his secret longings and hopes. Combining swashbuckling adventure with a moving story of human frailty and fortitude, The Confession of Sultana Daku is a grand narrative that is as mesmerizing as it is unsettling. Told with remarkable flair, passion and a rare sensitivity, it seals Sujit Saraf’s reputation as a master storyteller.

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Page Count

295

Publisher

Penguin UK

Published Date

ISBN 10

8184758286

ISBN 13

9788184758283

Autophagy Processes and Mechanisms

By: Rohan Dhiman,Sujit K. Bhutia

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Autophagy Processes and Mechanisms

By: Rohan Dhiman,Sujit K. Bhutia

Autophagy Processes and Mechanisms details the process of autophagy and its significance in diseases and aging. It provides insights into autophagy mechanisms and processes to broaden our understanding. By collecting recent progress on several aspects of the autophagy process, it provides a more integrative perspective and serves as a resource that can influence future research initiatives in the field. This new book is appropriate for basic and applied researchers in cell biology, biologists and those working in the pharmaceutical sciences. - Includes cutting-edge knowledge on autophagy processes as well as methodologies of research - Integrates knowledge from the perspectives of basic biological science, bioinformatics, clinical research and the pharmaceutical sciences - Provides an educational resource for students and investigators with an interest in autophagy, but who are not currently working in the field

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Page Count

244

Publisher

Academic Press

Published Date

ISBN 10

0323901433

ISBN 13

9780323901437

Bayesian Modeling of Spatio-Temporal Data with R

By: Sujit Sahu

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Bayesian Modeling of Spatio-Temporal Data with R

By: Sujit Sahu

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Solve jigsaw puzzle

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Page Count

385

Publisher

CRC Press

Published Date

ISBN 10

1000543692

ISBN 13

9781000543698

Introduction to Probability, Statistics and R

Foundations for Data-based Sciences

By: Sujit K. Sahu

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Introduction to Probability, Statistics and R

Foundations for Data-based Sciences

By: Sujit K. Sahu

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Write a review

Solve jigsaw puzzle

Age

Page Count

560

Publisher

Springer Nature

Published Date

ISBN 10

3031378652

ISBN 13

9783031378652

Book's by Sujit Sahu

Bayesian Modeling of Spatio-Temporal Data with R

Bayesian Modeling of Spatio-Temporal Data with R

Introduction to Probability, Statistics and R

Introduction to Probability, Statistics and R

Pharmaceutical Chemistry- Theory

Pharmaceutical Chemistry- Theory

The Confession of Sultana Daku

The Confession of Sultana Daku

Autophagy Processes and Mechanisms

Autophagy Processes and Mechanisms

Bayesian Modeling of Spatio-Temporal Data with R

Bayesian Modeling of Spatio-Temporal Data with R

Introduction to Probability, Statistics and R

Introduction to Probability, Statistics and R

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