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Bayesian Inference in the Social Sciences

, marketing, and finance Emphasizing interdisciplinary coverage, Bayesian Inference in the Social Sciences builds upon the recent growth in Bayesian;

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Applied Bayesian Modeling and Causal Inference from Incomplete Data Perspectives

scores, instrumental variables and Bayesian inference.* Includes a number of applications from the social and health sciences.* Edited and;

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Maximum Entropy and Bayesian Methods

inference in the natural and social sciences, engineering and medicine. Practical applications include: parametric model fitting and model selection;

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Bayesian Inference and Maximum Entropy Methods in Science and Engineering

, thermodynamics (equilibrium and non-equilibrium), particle physics, plasma physics, quantum mechanics, robotics, and the social sciences. Bayesian;

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Bayesian inference with INLA

, education, environmental science, epidemiology, public health, and the social sciences.;

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Bayesian inference with INLA

, education, environmental science, epidemiology, public health, and the social sciences.;

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Bayesian Modeling Using WinBUGS

, medicine, and the social sciences who use WinBUGS in their everyday work.;

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An Introduction to Bayesian Inference in Econometrics

This is a classical reprint edition of the original 1971 edition of An Introduction to Bayesian Inference in Economics. This historical;

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Statistical Inference

approach used throughout. It presents Bayesian versions of one- and two-sample t-tests, along with the corresponding normal variance tests. The;

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Statistical Inference

approach used throughout. It presents Bayesian versions of one- and two-sample t-tests, along with the corresponding normal variance tests. The;

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Topics on Methodological and Applied Statistical Inference

, likelihood approach, pseudo-likelihoods, regression, time series, and data analysis as well as applications in the life and social sciences. The;

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Topics on Methodological and Applied Statistical Inference

, likelihood approach, pseudo-likelihoods, regression, time series, and data analysis as well as applications in the life and social sciences. The;

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Bayesian Inference for Probabilistic Risk Assessment

scripts similar to a macro-type programming language. Accompanying most scripts is a graphical Bayesian network illustrating the elements of the;

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Missing Data in Longitudinal Studies

discusses key ideas in Bayesian inference, including specifying prior distributions, computing posterior distribution, and assessing model fit. The;

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Likelihood & Bayesian Inference

likelihood ratio and the Wald statistic. In the second part of the book, likelihood is combined with prior information to perform Bayesian inference;

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Likelihood and Bayesian Inference

likelihood ratio and the Wald statistic. In the second part of the book, likelihood is combined with prior information to perform Bayesian inference;

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Statistics for Astrophysics

in their field. This covers the foundations of Bayesian inference, Markov chain Monte Carlo technique, model building, Approximate Bayesian;

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Introduction to Bayesian Statistics

book on elementary Bayesian inference, and the material is easily accessible. It is both concise and timely, and provides a good collection;

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Bayesian Statistics for the Social Sciences

Bridging the gap between traditional classical statistics and a Bayesian approach, David Kaplan provides readers with the concepts and;

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Bayesian Methods For Hackers

. Bayesian Methods for Hackers illuminates Bayesian inference through probabilistic programming with the powerful PyMC language and the closely;

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Bayesian Statistics for Beginners

scientific method in general and is widely used across the social, biological, medical, and physical sciences. Key to this book's novel and informal;

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Bayesian Programming

. Written by the team who designed and implemented an efficient probabilistic inference engine to interpret Bayesian programs, the book offers many;

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Bayesian Programming

. Written by the team who designed and implemented an efficient probabilistic inference engine to interpret Bayesian programs, the book offers many;

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Contemporary Bayesian and Frequentist Statistical Research Methods for Natural Resource Scientists

The first all-inclusive introduction to modern statistical research methods in the natural resource sciences The use of Bayesian;

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Probability and Bayesian Modeling

distributions, and joint distributions. Statistical inference is presented completely from a Bayesian perspective. The text introduces inference and;

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BAYESIAN INFERENCE

This two volume set is a collection of 30 classic papers presenting ideas which have now become standard in the field of Bayesian inference;

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Bayesian Theory and Methods with Applications

, environmental sciences, business and economics and social sciences, among others. This book explores the use of Bayesian analysis in the statistical;

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