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Model Selection
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Philosophy of Statistics
Statisticians and philosophers of science have many common interests but restricted communication with each other. This volume aims to remedy these shortcomings. It provides state-of-the-art research in the area of philosophy of statistics by encouraging numerous experts to communicate with one another without feeling "restricted by their disciplines or thinking "piecemeal in their treatment of issues. A second goal of this book is to present work in the field without bias toward any particular statistical paradigm. Broadly speaking, the essays in this Handbook are concerned with problems of induction, statistics and probability. For centuries, foundational problems like induction have been among philosophers' favorite topics; recently, however, non-philosophers have increasingly taken a keen interest in these issues. This volume accordingly contains papers by both philosophers and non-philosophers, including scholars from nine academic disciplines. - Provides a bridge between philosophy and current scientific findings - Covers theory and applications - Encourages multi-disciplinary dialogue
Bayesian Thinking, Modeling and Computation
This volume describes how to develop Bayesian thinking, modelling and computation both from philosophical, methodological and application point of view. It further describes parametric and nonparametric Bayesian methods for modelling and how to use modern computational methods to summarize inferences using simulation. The book covers wide range of topics including objective and subjective Bayesian inferences with a variety of applications in modelling categorical, survival, spatial, spatiotemporal, Epidemiological, software reliability, small area and micro array data. The book concludes with a chapter on how to teach Bayesian thoughts to nonstatisticians. Critical thinking on causal effects Objective Bayesian philosophy Nonparametric Bayesian methodology Simulation based computing techniques Bioinformatics and Biostatistics
Probability Matching Priors: Higher Order Asymptotics
This is the first book on the topic of probability matching priors. It targets researchers, Bayesian and frequentist; graduate students in Statistics.
High-Value Plants
High-value crops are non-staple plants that give much higher return per unit area than staple crops. Diversification and investing towards high-value crops can potentially increase farm incomes, making them more attractive to new and small-scale agricultural farmers. This new volume explores the biotechnological applications for the unique high-value crops in response to the impending high-value agriculture revolution. The book discusses traditional knowledge, nutritional value, phytochemical activity, value addition quality, and postharvest management of some select unique high-value crops, including black ginger, bastard oleaster, Swertia chirayita, Garcinia, Parkia timoriana (or tree bean), king chili, Chenopodium (or goosefoot), sea buckthorn, broom grass, lichens, and others. High-Value Plants: Novel Insights and Biotechnological Advances provides important information for small-scale farmers and agricultural and horticultural professionals to consider diversifying into non-traditional, high-value, agricultural crops, an important area of potential income growth in rural areas.
Catalogue of Books
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Objective Bayesian Inference
Bayesian analysis is today understood to be an extremely powerful method of statistical analysis, as well an approach to statistics that is particularly transparent and intuitive. It is thus being extensively and increasingly utilized in virtually every area of science and society that involves analysis of data.A widespread misconception is that Bayesian analysis is a more subjective theory of statistical inference than what is now called classical statistics. This is true neither historically nor in practice. Indeed, objective Bayesian analysis dominated the statistical landscape from roughly 1780 to 1930, long before 'classical' statistics or subjective Bayesian analysis were developed. It...
Notable Modern Indian Mathematicians and Statisticians
This book provides a comprehensive portrayal of the history of Indian mathematicians and statisticians and uncovers many missing parts of the scientific representation of mathematical and statistical research during the 19th and 20th centuries of Bengal (now West Bengal), India. This book gives a brief historical account about the establishment of the first-two departments in an Indian university, where graduate teaching and research were initiated. This was a unique distinction for the University of Calcutta which was established in 1857. The creation of the world famous Indian Statistical Institute (ISI) in Calcutta (now Kolkata) is also briefly described. The lives and works of the 16 pio...
The Bayesian Choice
This is an introduction to Bayesian statistics and decision theory, including advanced topics such as Monte Carlo methods. This new edition contains several revised chapters and a new chapter on model choice.
