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Zustand: New. pp. iv + 113.

Hardcover. Zustand: New. Contents: Preface. 1. Introduction. 2. Evaluation of the study. 3. Chin society: the historical background. 4. Identity and history of the Chin. 5. Profile of social changes in the Chin society. 6. The Ramayana tradition of the northeast and beyond. 7. Burmese Ramayana: tradition and the Chin. 8. From Chin Hills to Mizoram. 9. In search of an identity. 10. Conclusion. Bibliography. Index. History helps people to know about their neighbours and thus helps them to find out affinities and differences, which not only satisfy the scholarly goals of knowledge production and education but also those of pragmatic understanding of values and attitudes for cultural exchange and survival of man as members of national and international community. What is important about north east is that it is predominantly tribal in its population structure and cultural practices. The challenge before us is to historically determine the exact influence of Ram Katha tradition on the culture of north east. It is a fact that the influences of the great epic have been carried to the north east through different routes. There are evidences that the Thai and the Mayanmarese (Burmese) versions of the Ramayana reached north east because of the migration of the Ahoms, who are believed to have migrated from Burma (Myanmar). Therefore the Ram Katha tradition in north east has diverse roots. Ramayana has always been a great source of inspiration to the whole of humanity. The present volume is a study of the Ramayana tradition among the Chin tribe inhabiting the borders of Mizoram, Manipur and Myanmar. Although the Chin tribe has a lot of cultural and other similarities with many other tribes on both sides of the border, they also do have marked differences with the tribes living in the surrounding plains and valleys. This tribe has a fairly isolated existence for centuries and its language is unwritten. Historically, it is extremely difficult to determine the exact period when the events of Ramayana got ingrained into Chin-Lushai culture. However, it may be traced to the Pan- Hindu influences and cultural contacts of early India with countries of South East Asia and far east and from there the influences were carried to the Chin and Lushai Hills. The Hindu relies in Indonesia, Thailand and Combodia bear testimony to this influence. It is hoped that the volume will be useful to researchers and scholars who are interested in the tribes of northeast and their cultures. (jacket). …

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Zustand: New. pp. xvi + 248 Acknowledgements.

Zustand: New. vi, 179 pages, illustrations black and white, and colour.

Zustand: New. vi, 179 pages, illustrations black and white, and colour.

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Hardcover. Zustand: New. The book Ramayana in the North-East India is a collection of papers presented at a National Seminar, the first of its kind in this part of the country on "Ramayana in North-East" held at Silchar organised by the Bharatiya Ithihasa Sankalana Samiti to examine and discuss various aspects of Ramakatha tradition in the North-East India. The papers have been contributed by well known scholars from Assam, Manipur, Meghalaya, Mizoram and Tripura and are highly illuminating in exploring the Ramakatha tradition in North-East India. It is hoped that the book will be well received by enlightened readers, and our efforts in the direction of rediscovering Ramakatha tradition in the North-East India will be most fruitful.…

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Zustand: New. Dipak K. Dey, Sujit K. Ghosh , Bani K. MallickThis volume describes how to conceptualize, perform, and critique traditional generalized linear models (GLMs) from a Bayesian perspective and how to use modern computational methods to summarize inferen.

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Hardback. Zustand: New. Bayesian Statistical Methods: With Applications to Machine Learning provides data scientists with the foundational and computational tools needed to carry out a Bayesian analysis. Compared to others, this book is more focused on Bayesian methods applied routinely in practice, including multiple linear regression, mixed effects models and generalized linear models. This second edition includes a new chapter on Bayesian machine learning methods to handle large and complex datasets and several new applications to illustrate the benefits of the Bayesian approach in terms of uncertainty quantification. Readers familiar with only introductory statistics will find this book accessible, as it includes many worked examples with complete R code, and comparisons are presented with analogous frequentist procedures. The book can be used as a one-semester course for advanced undergraduate and graduate students and can be used in courses comprising undergraduate statistics majors, as well as non-statistics graduate students from other disciplines such as engineering, ecology and psychology. In addition to thorough treatment of the basic concepts of Bayesian inferential methods, the book covers many general topics:Advice on selecting prior distributionsComputational methods including Markov chain Monte Carlo (MCMC) samplingModel-comparison and goodness-of-fit measures, including sensitivity to priors.To illustrate the flexibility of the Bayesian approaches for complex data structures, the latter chapters provide case studies covering advanced topics:Handling of missing and censored dataPriors for high-dimensional regression modelsMachine learning models including Bayesian adaptive regression trees and deep learningComputational techniques for large datasetsFrequentist properties of Bayesian methods.The advanced topics are presented with sufficient conceptual depth that the reader will be able to carry out such analysis and argue the relative merits of Bayesian and classical methods. A repository of R code, motivating data sets and complete data analyses is made available on the book's website. …

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Hardback. Zustand: New. Bayesian Statistical Methods: With Applications to Machine Learning provides data scientists with the foundational and computational tools needed to carry out a Bayesian analysis. Compared to others, this book is more focused on Bayesian methods applied routinely in practice, including multiple linear regression, mixed effects models and generalized linear models. This second edition includes a new chapter on Bayesian machine learning methods to handle large and complex datasets and several new applications to illustrate the benefits of the Bayesian approach in terms of uncertainty quantification. Readers familiar with only introductory statistics will find this book accessible, as it includes many worked examples with complete R code, and comparisons are presented with analogous frequentist procedures. The book can be used as a one-semester course for advanced undergraduate and graduate students and can be used in courses comprising undergraduate statistics majors, as well as non-statistics graduate students from other disciplines such as engineering, ecology and psychology. In addition to thorough treatment of the basic concepts of Bayesian inferential methods, the book covers many general topics:Advice on selecting prior distributionsComputational methods including Markov chain Monte Carlo (MCMC) samplingModel-comparison and goodness-of-fit measures, including sensitivity to priors.To illustrate the flexibility of the Bayesian approaches for complex data structures, the latter chapters provide case studies covering advanced topics:Handling of missing and censored dataPriors for high-dimensional regression modelsMachine learning models including Bayesian adaptive regression trees and deep learningComputational techniques for large datasetsFrequentist properties of Bayesian methods.The advanced topics are presented with sufficient conceptual depth that the reader will be able to carry out such analysis and argue the relative merits of Bayesian and classical methods. A repository of R code, motivating data sets and complete data analyses is made available on the book's website. …