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Author: Gauri Sankar Datta
Publisher: Springer Science & Business Media
ISBN: 146122036X
Category : Mathematics
Languages : en
Pages : 138
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Book Description
This is the first book on the topic of probability matching priors. It targets researchers, Bayesian and frequentist; graduate students in Statistics.
Author: Gauri Sankar Datta
Publisher: Springer Science & Business Media
ISBN: 146122036X
Category : Mathematics
Languages : en
Pages : 138
Get Book Here
Book Description
This is the first book on the topic of probability matching priors. It targets researchers, Bayesian and frequentist; graduate students in Statistics.
Author: Gauri Sankar Datta
Publisher:
ISBN: 9781461220374
Category :
Languages : en
Pages : 144
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Book Description
Author: Subhashis Ghosal
Publisher:
ISBN:
Category :
Languages : en
Pages : 16
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Book Description
Author: Upasana Santra
Publisher:
ISBN:
Category :
Languages : en
Pages :
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Book Description
There however, does not exist a prior that satisfies the matching via distribution functions criterion in this case. Finally, a general class of priors have been obtained for inference about the ratio of standard deviations. The propriety of the resultant posteriors is proved in each case under mild conditions and simulation results suggest that the approximations are valid even for moderate sample sizes. Further, several likelihood based methods have been considered for the correlation coefficient. One common feature of all these modified likelihoods is that they are all dependent on the data only through the sample correlation coefficient r.
Author: Daniel R. Eno
Publisher:
ISBN:
Category : Calibration
Languages : en
Pages : 25
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Book Description
Author: Dharini Pathmanathan
Publisher:
ISBN:
Category : Bayesian statistical decision theory
Languages : en
Pages : 312
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Book Description
Author: Ji-qian Fang
Publisher: #N/A
ISBN: 9813148977
Category : Medical
Languages : en
Pages : 852
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Book Description
This unique volume focuses on the 'tools' of medical statistics. It contains over 500 concepts or methods, all of which are explained very clearly and in detail.Each chapter focuses on a specific field and its applications. There are about 20 items in each chapter with each item independent of one another and explained within one page (plus references). The structure of the book makes it extremely handy for solving targeted problems in this area.As the goal of the book is to encourage students to learn more combinatorics, every effort has been made to provide them with a not only useful, but also enjoyable and engaging reading.This handbook plays the role of 'tutor' or 'advisor' for teaching and further learning. It can also be a useful source for 'MOOC-style teaching'.
Author:
Publisher: Elsevier
ISBN: 0080461174
Category : Mathematics
Languages : en
Pages : 1062
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Book Description
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
Author: Arijit Chaudhuri
Publisher:
ISBN: 9789813369924
Category :
Languages : en
Pages : 0
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Book Description
This book includes speeches given during five seminar sessions held in honor of Prof. C. R. Rao, on his 100th year. This book also contains a few write-ups touching on the diverse aspects of this august personality. The chapters pay tribute to Prof. C. R. Rao, the Padma Vibhushan awardee, by discussing his life and contributions to the field of statistics. The book also includes a chapter by the Abel Prize winner Prof. S. R. Varadhan who happened to successfully complete his Ph.D. under the guidance of Prof. C. R. Rao. .
Author: Jim Albert
Publisher: CRC Press
ISBN: 1351030132
Category : Mathematics
Languages : en
Pages : 553
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Book Description
Probability and Bayesian Modeling is an introduction to probability and Bayesian thinking for undergraduate students with a calculus background. The first part of the book provides a broad view of probability including foundations, conditional probability, discrete and continuous distributions, and joint distributions. Statistical inference is presented completely from a Bayesian perspective. The text introduces inference and prediction for a single proportion and a single mean from Normal sampling. After fundamentals of Markov Chain Monte Carlo algorithms are introduced, Bayesian inference is described for hierarchical and regression models including logistic regression. The book presents several case studies motivated by some historical Bayesian studies and the authors’ research. This text reflects modern Bayesian statistical practice. Simulation is introduced in all the probability chapters and extensively used in the Bayesian material to simulate from the posterior and predictive distributions. One chapter describes the basic tenets of Metropolis and Gibbs sampling algorithms; however several chapters introduce the fundamentals of Bayesian inference for conjugate priors to deepen understanding. Strategies for constructing prior distributions are described in situations when one has substantial prior information and for cases where one has weak prior knowledge. One chapter introduces hierarchical Bayesian modeling as a practical way of combining data from different groups. There is an extensive discussion of Bayesian regression models including the construction of informative priors, inference about functions of the parameters of interest, prediction, and model selection. The text uses JAGS (Just Another Gibbs Sampler) as a general-purpose computational method for simulating from posterior distributions for a variety of Bayesian models. An R package ProbBayes is available containing all of the book datasets and special functions for illustrating concepts from the book. A complete solutions manual is available for instructors who adopt the book in the Additional Resources section.
